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20 pages, 8781 KiB  
Article
A Virtual View Acquisition Technique for Complex Scenes of Monocular Images Based on Layered Depth Images
by Qi Wang and Yan Piao
Appl. Sci. 2024, 14(22), 10557; https://doi.org/10.3390/app142210557 - 15 Nov 2024
Viewed by 492
Abstract
With the rapid development of stereoscopic display technology, how to generate high-quality virtual view images has become the key in the applications of 3D video, 3D TV and virtual reality. The traditional virtual view rendering technology maps the reference view into the virtual [...] Read more.
With the rapid development of stereoscopic display technology, how to generate high-quality virtual view images has become the key in the applications of 3D video, 3D TV and virtual reality. The traditional virtual view rendering technology maps the reference view into the virtual view by means of 3D transformation, but when the background area is occluded by the foreground object, the content of the occluded area cannot be inferred. To solve this problem, we propose a virtual view acquisition technique for complex scenes of monocular images based on a layered depth image (LDI). Firstly, the depth discontinuities of the edge of the occluded area are reasonably grouped by using the multilayer representation of the LDI, and the depth edge of the occluded area is inpainted by the edge inpainting network. Then, the generative adversarial network (GAN) is used to fill the information of color and depth in the occluded area, and the inpainting virtual view is generated. Finally, GAN is used to optimize the color and depth of the virtual view, and the high-quality virtual view is generated. The effectiveness of the proposed method is proved by experiments, and it is also applicable to complex scenes. Full article
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<p>The overall frame of virtual viewpoint image generation.</p>
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<p>Depth images of various types generated by the method proposed in this paper.</p>
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<p>Depth image preprocessing. (<b>a</b>) The input RGB image. (<b>b</b>) Depth image after filtering. (<b>c</b>) The enlarged image of the red box area in (<b>b</b>); (<b>d</b>) The preprocessed image of (<b>c</b>). (<b>e</b>) The image of lines with discontinuous depth.</p>
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<p>The area division of the input RGB image. (<b>a</b>) The input RBG image. (<b>b</b>) Generated virtual view image without inpainting. The pink area is the foreground area, the gray area is the occluded area, and the blue area is the background area.</p>
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<p>The framework of the edge inpainting network.</p>
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<p>The framework of virtual view optimization network.</p>
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<p>Virtual viewpoint images generated at different positions. (<b>a</b>) The images of the standard model established by C4D, and the model position is x = 0; (<b>b</b>) Viewpoint images of the model generated by C4D at x = −3; (<b>c</b>) The virtual viewpoint images of the model estimated by the method in this paper at x = −3; (<b>d</b>) Viewpoint images of the model generated by C4D at x = +3; (<b>e</b>) Virtual viewpoint images of the model estimated by the method in this paper at x = +3.</p>
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<p>Camera distributions.</p>
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<p>Generated virtual viewpoint images using the ballet image sequence. (<b>a</b>) The input image, which is the 10th frame taken by Cam4. (<b>b</b>) The 10th frame image taken by Cam3. (<b>c</b>) The 10th frame image taken by Cam5. (<b>d</b>) The 10th frame image of Cam3, which is generated by the method in this paper. (<b>e</b>) The 10th frame image of Cam5, which is generated by the method in this paper.</p>
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<p>Generated virtual viewpoint images using the breakdancers image sequence. (<b>a</b>) The input image, which is the 20th frame taken by Cam4. (<b>b</b>) The 20th frame image taken by Cam3. (<b>c</b>) The 20th frame image taken by Cam5. (<b>d</b>) The 20th frame image of Cam3, which is generated by the method in this paper. (<b>e</b>) The 20th frame image of Cam5, which is generated by the method in this paper.</p>
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<p>Different types of virtual viewpoint images rendered by the method in this paper.</p>
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<p>Rendered virtual viewpoint images. (<b>a</b>) Input images; (<b>b</b>) The partial enlarged images of the contents in red boxes in (<b>a</b>); (<b>c</b>) The true images of virtual viewpoint images (<b>d</b>,<b>e</b>); (<b>d</b>) Virtual viewpoint images generated by the method of [<a href="#B58-applsci-14-10557" class="html-bibr">58</a>]; (<b>e</b>) Virtual viewpoint images generated by the method in this paper.</p>
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13 pages, 4104 KiB  
Article
Abutment Tooth Formation Simulator for Naked-Eye Stereoscopy
by Rintaro Tomita, Akito Nakano, Norishige Kawanishi, Noriyuki Hoshi, Tomoki Itamiya and Katsuhiko Kimoto
Appl. Sci. 2024, 14(18), 8367; https://doi.org/10.3390/app14188367 - 17 Sep 2024
Viewed by 896
Abstract
Virtual reality is considered to be useful in improving procedural skills in dental education, but systems using wearable devices such as head-mounted displays (HMDs) have many problems in terms of long-term use and hygiene, and the accuracy of stereoscopic viewing at close ranges [...] Read more.
Virtual reality is considered to be useful in improving procedural skills in dental education, but systems using wearable devices such as head-mounted displays (HMDs) have many problems in terms of long-term use and hygiene, and the accuracy of stereoscopic viewing at close ranges is inadequate. We developed an abutment tooth formation simulator that utilizes a display (spatial reality display—SRD) to precisely reproduce 3D space with naked-eye stereoscopic viewing at close range. The purpose of this was to develop and validate the usefulness of an abutment tooth formation simulator using an SRD. A 3D-CG (three-dimensional computer graphics) dental model that can be cut in real time was output to the SRD, and an automatic quantitative scoring function was also implemented by comparing the cutting results with exemplars. Dentists in the department of fixed prosthodontics performed cutting operations on both a 2D display-based simulator and an SRD-based simulator and conducted a 5-point rating feedback survey. Compared to the simulator that used a 2D display, the measurements of the simulator using an SRD were significantly more accurate. The SRD-based abutment tooth formation simulator received a positive technical evaluation and high dentist satisfaction (4.37), suggesting its usefulness and raising expectations regarding its future application in dental education. Full article
(This article belongs to the Special Issue Digital Dentistry and Oral Health)
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<p>Flow of abutment tooth formation.</p>
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<p>(<b>a</b>) Conventional dental training using mannequins; (<b>b</b>) conventional VR abutment formation training (using HMDs or 2D displays); and (<b>c</b>) VR training using naked-eye stereoscopic displays.</p>
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<p>(<b>a</b>) Displaying a 3D-CG model before abutment tooth formation. Exemplars can be placed in the tooth model for confirmation (light blue objects); (<b>b</b>) in the process of forming abutment teeth; (<b>c</b>) visual evaluation after automatic scoring. The amount of formation can be divided by color for feedback(Red is Over-formation, Blue is Under-formation ).</p>
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<p>(<b>a</b>) STL data obtained using an intraoral scanner; (<b>b</b>) voxelized model with added spatial information. The pink dots are the coordinates of the midpoint of each Voxel; (<b>c</b>) result of voxel-to-CG model conversion using the marching cubes algorithm.</p>
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<p>(<b>a</b>) The exemplar model is observed through a clairvoyant. The red object is an exemplar; (<b>b</b>) overly formed model; (<b>c</b>) the visualization of over- or under-formation after automatic scoring (blue: under-formed; red: over-formed).</p>
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<p>Flow of verification of actual equipment. After obtaining consent, validation will be performed on both displays, followed by automatic scoring and a questionnaire survey.</p>
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<p>(<b>a</b>) A view of the dentist’s hand verifying the results; (<b>b</b>) voxelized model with added spatial information.</p>
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<p>Comparison of objective endpoints using SRD and 2D displays (* <span class="html-italic">p</span> &lt; 0.01).</p>
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29 pages, 923 KiB  
Review
Light Field Visualization for Training and Education: A Review
by Mary Guindy and Peter A. Kara
Electronics 2024, 13(5), 876; https://doi.org/10.3390/electronics13050876 - 24 Feb 2024
Cited by 2 | Viewed by 1206
Abstract
Three-dimensional visualization technologies such as stereoscopic 3D, virtual reality, and augmented reality have already emerged in training and education; however, light field displays are yet to be introduced in such contexts. In this paper, we characterize light field visualization as a potential candidate [...] Read more.
Three-dimensional visualization technologies such as stereoscopic 3D, virtual reality, and augmented reality have already emerged in training and education; however, light field displays are yet to be introduced in such contexts. In this paper, we characterize light field visualization as a potential candidate for the future of training and education, and compare it to other state-of-the-art 3D technologies. We separately address preschool and elementary school education, middle and high school education, higher education, and specialized training, and assess the suitability of light field displays for these utilization contexts via key performance indicators. This paper exhibits various examples for education, and highlights the differences in terms of display requirements and characteristics. Additionally, our contribution analyzes the scientific-literature-related trends of the past 20 years for 3D technologies, and the past 5 years for the level of education. While the acquired data indicates that light field is still lacking in the context of education, general research on the visualization technology is steadily rising. Finally, we specify a number of future research directions that shall contribute to the emergence of light field visualization for training and education. Full article
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<p>Number of articles (A) and reviews (R) in WoS (W) and SCOPUS (S) for 3D visualization technologies in the context of education in the past 20 years.</p>
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<p>Distribution of articles (A) and reviews (R) in WoS (W) and SCOPUS (S) for 3D visualization technologies in the context of education in the past 20 years.</p>
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<p>Combination of keywords for database analyses.</p>
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<p>Light field visualization with a greater FOV for multiple simultaneous observers (<b>left</b>), and with a smaller FOV for a single observer (<b>right</b>).</p>
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<p>Light field visualization with a greater FOV for multiple simultaneous static observers (<b>left</b>), and with a greater FOV for a single mobile observer (<b>right</b>).</p>
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<p>Number of articles (A) and reviews (R) in WoS for 3D visualization technologies in the context of education in the past 5 years.</p>
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<p>Number of articles (A) and reviews (R) in SCOPUS for 3D visualization technologies in the context of education in the past 5 years.</p>
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<p>Number of articles (A) and reviews (R) on light field (LF) and light field display (LFD) in WoS (W) and SCOPUS (S) in the past 20 years.</p>
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<p>The life cycle of a caterpillar on an LFD.</p>
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<p>Using an LFD for studying stratification.</p>
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14 pages, 4325 KiB  
Review
Recent Progress in True 3D Display Technologies Based on Liquid Crystal Devices
by Shuxin Liu, Yan Li and Yikai Su
Crystals 2023, 13(12), 1639; https://doi.org/10.3390/cryst13121639 - 27 Nov 2023
Cited by 1 | Viewed by 2553
Abstract
In recent years, the emergence of virtual reality (VR) and augmented reality (AR) has revolutionized the way we interact with the world, leading to significant advancements in 3D display technology. However, some of the currently employed 3D display techniques rely on stereoscopic 3D [...] Read more.
In recent years, the emergence of virtual reality (VR) and augmented reality (AR) has revolutionized the way we interact with the world, leading to significant advancements in 3D display technology. However, some of the currently employed 3D display techniques rely on stereoscopic 3D display method, which may lead to visual discomfort due to the vergence-accommodation conflict. To address this issue, several true 3D technologies have been proposed as alternatives, including multi-plane displays, holographic displays, super multi-view displays, and integrated imaging displays. In this review, we focus on planar liquid crystal (LC) devices for different types of true 3D display applications. Given the excellent optical performance of the LC devices, we believe that LC devices hold great potential for true 3D displays. Full article
(This article belongs to the Special Issue Liquid Crystals and Devices)
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<p>Vergence and accommodation conflict (VAC) problem. (<b>a</b>) Natural 3D visual experience in real world. (<b>b</b>) VAC in conventional displays.</p>
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<p>(<b>a</b>,<b>b</b>) PSLC film in a transparent state and a scattering state when the applied voltage is on and off [<a href="#B19-crystals-13-01639" class="html-bibr">19</a>], respectively. (<b>c</b>) Optical scheme of the AR system based on PSLC scattering shutters. (<b>d</b>) Ray tracing diagram of the quasi-collimated projector. (<b>e</b>–<b>h</b>) Demonstration of the AR display prototype with four letters “A,B,C,D” when focusing the camera at 30 cm, 50 cm, 80 cm, and 500 cm [<a href="#B19-crystals-13-01639" class="html-bibr">19</a>], respectively. (<b>a</b>,<b>b</b>,<b>e</b>–<b>h</b>) adapted with permission from Ref. [<a href="#B19-crystals-13-01639" class="html-bibr">19</a>], Wiley.</p>
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<p>(<b>a</b>) Multi-plane display based on a refractive LC lens. (<b>b</b>) Structure of the refractive LC lens.</p>
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<p>Four-plane display based on two PBLC lenses. (<b>a</b>–<b>d</b>) Four modes: f = 50 cm, 100 cm, ∞, and −100 cm when the applied voltages on P1 and P2 are off|off, off|on, on|on, and on|off, respectively [<a href="#B40-crystals-13-01639" class="html-bibr">40</a>]. (<b>e</b>–<b>h</b>) Beam spots on the RS when f = 50 cm, 100 cm, ∞, and −100 cm, respectively. RS: receiving screen [<a href="#B40-crystals-13-01639" class="html-bibr">40</a>]. (<b>i</b>–<b>l</b>) Four letters, “SJTU”, are rendered at distances of 28 cm, 40 cm, 67 cm, and 200 cm [<a href="#B40-crystals-13-01639" class="html-bibr">40</a>]. (<b>a</b>–<b>l</b>) adapted with permission from Ref. [<a href="#B40-crystals-13-01639" class="html-bibr">40</a>], Optica Publishing Group.</p>
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<p>(<b>a</b>–<b>c</b>) Virtual image “G” is rendered at 40 cm with a lateral shift of x<sub>0</sub> = −5 mm when focusing the camera at 40 cm, 55 cm, and 70 cm [<a href="#B42-crystals-13-01639" class="html-bibr">42</a>], respectively. (<b>d</b>–<b>f</b>) Virtual image “G” is rendered at 55 cm with a lateral shift of x<sub>0</sub> = 0 when focusing the camera at 40 cm, 55 cm, and 70 cm [<a href="#B42-crystals-13-01639" class="html-bibr">42</a>], respectively. (<b>g</b>–<b>i</b>) Virtual image “G” is rendered at 70 cm with a lateral shift of x<sub>0</sub> = 2.4 mm when focusing the camera at 40 cm, 55 cm, and 70 cm [<a href="#B42-crystals-13-01639" class="html-bibr">42</a>], respectively. (<b>j</b>) Scheme of multi-plane display based on planar Alvarez tunable LC lens. (<b>a</b>–<b>i</b>) adapted with permission from Ref. [<a href="#B42-crystals-13-01639" class="html-bibr">42</a>], Optica Publishing Group.</p>
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<p>(<b>a</b>) AR display based on two CLC films with opposite handedness. Red arrow is the LCP light, and green arrow is RCP light. (<b>b</b>,<b>c</b>) Color image is rendered at 20 cm when focusing the camera at 20 cm and 130 cm, respectively [<a href="#B26-crystals-13-01639" class="html-bibr">26</a>]. (<b>d</b>,<b>e</b>) Color image is rendered at 130 cm when focusing the camera at 130 cm and 20 cm [<a href="#B26-crystals-13-01639" class="html-bibr">26</a>], respectively. (<b>b</b>–<b>e</b>) adapted with permission from Ref. [<a href="#B26-crystals-13-01639" class="html-bibr">26</a>], Optica Publishing Group.</p>
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<p>Dual-plane AR display system utilizing a LC polarization switch and PBS.</p>
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<p>(<b>a</b>) SMV display based on PBLC grating array. (<b>b</b>,<b>c</b>) Microscopic images of 1D/2D PBLC grating arrays [<a href="#B55-crystals-13-01639" class="html-bibr">55</a>]. (<b>d</b>,<b>e</b>) Diffraction patterns from different regions of the 1D/2D PB LC grating arrays [<a href="#B55-crystals-13-01639" class="html-bibr">55</a>]. (<b>f</b>,<b>g</b>) SMV display with 1D PBLC grating when camera focusing at 20 cm and 160 cm. (<b>h</b>,<b>i</b>) SMV display with 2D PBLC when focusing camera at different distances [<a href="#B55-crystals-13-01639" class="html-bibr">55</a>]. (<b>b</b>–<b>i</b>) adapted with permission from Ref. [<a href="#B55-crystals-13-01639" class="html-bibr">55</a>], Creative Commons.</p>
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<p>(<b>a</b>) Principle of the azo dye-doped LC device. (<b>b</b>) Principle of the quantum dot-doped LC device. (<b>c</b>) Schematic of the real-time holographic display system based on LC device. (<b>d</b>) Snapshots using three R,G,B reading beams [<a href="#B62-crystals-13-01639" class="html-bibr">62</a>]. (<b>d</b>) adapted with permission from Ref. [<a href="#B62-crystals-13-01639" class="html-bibr">62</a>], Optica Publishing Group.</p>
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<p>(<b>a</b>) Schematic of the integral imaging. (<b>b</b>) Polarization-dependent LC lens array. (<b>c</b>) Schematic of the integral imaging based on LC lens array.</p>
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<p>(<b>a</b>) Al circular hole electrode. (<b>b</b>) Structure of LC lens array using patterned electrode.</p>
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19 pages, 5498 KiB  
Article
Integral Imaging Display System Based on Human Visual Distance Perception Model
by Lijin Deng, Zhihong Li, Yuejianan Gu and Qi Wang
Sensors 2023, 23(21), 9011; https://doi.org/10.3390/s23219011 - 6 Nov 2023
Cited by 1 | Viewed by 1565
Abstract
In an integral imaging (II) display system, the self-adjustment ability of the human eye can result in blurry observations when viewing 3D targets outside the focal plane within a specific range. This can impact the overall imaging quality of the II system. This [...] Read more.
In an integral imaging (II) display system, the self-adjustment ability of the human eye can result in blurry observations when viewing 3D targets outside the focal plane within a specific range. This can impact the overall imaging quality of the II system. This research examines the visual characteristics of the human eye and analyzes the path of light from a point source to the eye in the process of capturing and reconstructing the light field. Then, an overall depth of field (DOF) model of II is derived based on the human visual system (HVS). On this basis, an II system based on the human visual distance (HVD) perception model is proposed, and an interactive II display system is constructed. The experimental results confirm the effectiveness of the proposed method. The display system improves the viewing distance range, enhances spatial resolution and provides better stereoscopic display effects. When comparing our method with three other methods, it is clear that our approach produces better results in optical experiments and objective evaluations: the cumulative probability of blur detection (CPBD) value is 38.73%, the structural similarity index (SSIM) value is 86.56%, and the peak signal-to-noise ratio (PSNR) value is 31.12. These values align with subjective evaluations based on the characteristics of the human visual system. Full article
(This article belongs to the Collection 3D Imaging and Sensing System)
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<p>Interactive II display system based on HVD perception model.</p>
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<p>Discrete phenomena occur at off-focus point B.</p>
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<p>Facet braiding in II display.</p>
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<p>Optical path diagrams of single-lens imaging in the acquisition stage of II.</p>
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<p>Optical path diagrams of single-lens imaging in the reconstruction stage of II.</p>
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<p>Analysis of visual limits of the human eye.: (<b>a</b>) spatial resolution of the human eye; (<b>b</b>) line resolution of the human eye.</p>
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<p>Analysis of pixel acquisition.</p>
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<p>Displacement relationship of homonymous image points during the collection process.</p>
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<p>Analysis of pixel calibration.</p>
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<p>Workflow of the interactive II display system.</p>
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<p>Building image collection scene with 3Ds Max: (<b>a</b>) 3Ds Max simulated pixel collection scene; (<b>b</b>) collected EIA; (<b>c</b>) collected RGB image; (<b>d</b>) collected depth image.</p>
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<p>Optical reconstruction experimental platform: (<b>a</b>) optical experimental platform 1; (<b>b</b>) optical experimental platform 2.</p>
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<p>Overall DOF model verification experiment: (<b>a</b>) Collection Scene 1; (<b>b</b>) Collection Scene 2; (<b>c</b>) computer reconstruction of Scene 1; (<b>d</b>) computer reconstruction of Scene 2; (<b>e</b>) optical experiment of Scene 1; (<b>f</b>) optical experiment of Scene 2.</p>
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<p>Objective evaluations at different positions of reconstruction distance: (<b>a</b>) CPBD for reconstructed images; (<b>b</b>) SSIM for reconstructed images; (<b>c</b>) PSNR for reconstructed images.</p>
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<p>Optical reconstruction experimental results: (<b>a</b>) RODC algorithm [<a href="#B28-sensors-23-09011" class="html-bibr">28</a>]; (<b>b</b>) RIOP algorithm [<a href="#B29-sensors-23-09011" class="html-bibr">29</a>]; (<b>c</b>) LFR algorithm [<a href="#B30-sensors-23-09011" class="html-bibr">30</a>]; (<b>d</b>) our method.</p>
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<p>Optical reconstruction results of two types of pixels at <span class="html-italic">L</span> = 2 m.: (<b>a</b>) optical experiment before improvement; (<b>b</b>) optical experiment after improvement.</p>
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<p>Human face–eye detection and distance measurement device: (<b>a</b>) visual distance detection results; (<b>b</b>) custom binocular camera.</p>
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<p>Optical reconstruction at various viewing distances after improvement using the HVD perception model: (<b>a</b>) <span class="html-italic">L</span> = 2 m; (<b>b</b>) <span class="html-italic">L</span> = 2.74 m; (<b>c</b>) <span class="html-italic">L</span> = 4 m.</p>
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<p>Optical reconstruction at various viewing distances after improvement using the HVD perception model: (<b>a</b>) <span class="html-italic">L</span> = 2 m; (<b>b</b>) <span class="html-italic">L</span> = 2.74 m; (<b>c</b>) <span class="html-italic">L</span> = 4 m.</p>
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22 pages, 17347 KiB  
Article
How Do Background and Remote User Representations Affect Social Telepresence in Remote Collaboration?: A Study with Portal Display, a Head Pose-Responsive Video Teleconferencing System
by Seongjun Kang, Gwangbin Kim, Kyung-Taek Lee and SeungJun Kim
Electronics 2023, 12(20), 4339; https://doi.org/10.3390/electronics12204339 - 19 Oct 2023
Viewed by 1415
Abstract
This study presents Portal Display, a screen-based telepresence system that mediates the interaction between two distinct spaces, each using a single display system. The system synchronizes the users’ viewpoint with their head position and orientation to provide stereoscopic vision through this single monitor. [...] Read more.
This study presents Portal Display, a screen-based telepresence system that mediates the interaction between two distinct spaces, each using a single display system. The system synchronizes the users’ viewpoint with their head position and orientation to provide stereoscopic vision through this single monitor. This research evaluates the impact of graphically rendered and video-streamed backgrounds and remote user representations on social telepresence, usability, and concentration during conversations and collaborative tasks. Our results indicate that the type of background has a negligible impact on these metrics. However, point cloud streaming of remote users significantly improves social telepresence, usability, and concentration compared with graphical avatars. This study implies that Portal Display can operate more efficiently by substituting the background with graphical rendering and focusing on higher-resolution 3D point cloud streaming for narrower regions for remote user representations. This configuration may be especially advantageous for applications where the remote user’s background is not essential to the task, potentially enhancing social telepresence. Full article
(This article belongs to the Special Issue Perception and Interaction in Mixed, Augmented, and Virtual Reality)
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<p>Comprehensive research design, highlighting the development, experimental, and analytical phases.</p>
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<p>Linear transformation of the scene aligning symmetrically with the user’s head position.</p>
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<p>Step-by-step composite linear transformation process within the 3D engine space.</p>
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<p>Changes in the stereoscopic vision of the scene according to the user’s head position.</p>
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<p>Indoor environment with (<b>a</b>) point cloud streaming and (<b>b</b>) graphical rendering.</p>
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<p>Remote users represented with (<b>a</b>) point cloud streaming and (<b>b</b>) graphical rendering.</p>
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<p>Point cloud streaming from a depth camera capturing a remote user, with background removed using band-pass filters.</p>
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<p>Inverse kinematics to transform the end-effector joint positions (facial landmarks and upper body) into avatar motions.</p>
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<p>Network architecture for the Portal Display system, detailing data flow across processes.</p>
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<p>Portal Display setup in two separate environments.</p>
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<p>Four different conditions (two backgrounds × two remote users) used in the experiment.</p>
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<p>Usability, social telepresence, and concentration questions in the user questionnaire.</p>
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<p>Results of SUS score (Condition 1: PCD remote user + PCD background, Condition 2: PCD remote user + graphic background, Condition 3: graphic remote user + PCD background, Condition 4: graphic remote user + graphic background) (** <span class="html-italic">p</span> &lt; 0.01).</p>
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<p>Results of social telepresence (Condition 1: PCD remote user + PCD background, Condition 2: PCD remote user + graphic background, Condition 3: graphic remote user + PCD background, Condition 4: graphic remote user + graphic background) (*** <span class="html-italic">p</span> &lt; 0.001).</p>
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<p>Results of remote user concentration (Condition 1: PCD remote user + PCD background, Condition 2: PCD remote user + graphic background, Condition 3: graphic remote user + PCD background, Condition 4: graphic remote user + graphic background) (*** <span class="html-italic">p</span> &lt; 0.001).</p>
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<p>Scripts for topics such as culture, education, and content in virtual worlds.</p>
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<p>Car block models with similar levels of difficulty.</p>
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14 pages, 2826 KiB  
Article
Study of Root Canal Length Estimations by 3D Spatial Reproduction with Stereoscopic Vision
by Takato Tsukuda, Noriko Mutoh, Akito Nakano, Tomoki Itamiya and Nobuyuki Tani-Ishii
Appl. Sci. 2023, 13(15), 8651; https://doi.org/10.3390/app13158651 - 27 Jul 2023
Cited by 1 | Viewed by 1627
Abstract
Extended Reality (XR) applications are considered useful for skill acquisition in dental education. In this study, we examined the functionality and usefulness of an application called “SR View for Endo” that measures root canal length using a Spatial Reality Display (SRD) capable of [...] Read more.
Extended Reality (XR) applications are considered useful for skill acquisition in dental education. In this study, we examined the functionality and usefulness of an application called “SR View for Endo” that measures root canal length using a Spatial Reality Display (SRD) capable of naked-eye stereoscopic viewing. Three-dimensional computer graphics (3DCG) data of dental models were obtained and output to both the SRD and conventional 2D display devices. Forty dentists working at the Kanagawa Dental University Hospital measured root canal length using both types of devices and provided feedback through a questionnaire. Statistical analysis using one-way analysis of variance evaluated the measurement values and time, while multivariate analysis assessed the relationship between questionnaire responses and measurement time. There was no significant difference in the measurement values between the 2D device and SRD, but there was a significant difference in measurement time. Furthermore, a negative correlation was observed between the frequency of device usage and the extended measurement time of the 2D device. Measurements using the SRD demonstrated higher accuracy and shorter measurement times compared to the 2D device, increasing expectations for clinical practice in dental education and clinical education for clinical applications. However, a certain percentage of participants experienced symptoms resembling motion sickness associated with virtual reality (VR). Full article
(This article belongs to the Special Issue 3D Scene Understanding and Object Recognition)
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<p>The dental models used in this study. Maxillary first premolar (<b>a</b>), maxillary first molar (<b>b</b>), mandibular first molar (<b>c</b>), and mandibular first premolar (<b>d</b>).</p>
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<p>An example of a series of operations for measurement using SR View for Endo in the state where the 3D space reproduction (3DCG) environment is constructed by SRD. The SRD screen after setting the first reference point on the #26 tooth model (<b>a</b>). The SRD screen after setting the second reference point by right-clicking (<b>b</b>). The SRD screen after changing the angle after measurement (<b>c</b>).</p>
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<p>Measurement screen using the conventional 2D device. Screen (<b>a</b>) displaying the dental model of tooth number 26. Schematic diagram (<b>b</b>) of the 2D device measurement screen.</p>
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<p>Study design flow chart.</p>
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<p>Bland–Altman plots of the first and second measurements by the spatial reality display (SRD). Scatterplot of Bland–Altman limits for maxillary first premolar as measured by the SRD (<b>a</b>); maxillary first premolar measurements obtained by the SRD (<b>b</b>); scatterplot of Bland–Altman limits for maxillary first molar as measured by the SRD (<b>c</b>); maxillary first molar measurements obtained by the SRD (<b>d</b>); scatterplot of Bland–Altman limits for mandibular first premolar as measured by the SRD (<b>e</b>); mandibular first premolar measurements obtained by the SRD (<b>f</b>); scatterplot of Bland–Altman limits for mandibular first premolar as measured by the SRD (<b>g</b>); and mandibular first premolar measurements obtained by the SRD (<b>h</b>).</p>
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<p>Bland–Altman plots of the first and second measurements by the spatial reality display (SRD). Scatterplot of Bland–Altman limits for maxillary first premolar as measured by the SRD (<b>a</b>); maxillary first premolar measurements obtained by the SRD (<b>b</b>); scatterplot of Bland–Altman limits for maxillary first molar as measured by the SRD (<b>c</b>); maxillary first molar measurements obtained by the SRD (<b>d</b>); scatterplot of Bland–Altman limits for mandibular first premolar as measured by the SRD (<b>e</b>); mandibular first premolar measurements obtained by the SRD (<b>f</b>); scatterplot of Bland–Altman limits for mandibular first premolar as measured by the SRD (<b>g</b>); and mandibular first premolar measurements obtained by the SRD (<b>h</b>).</p>
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<p>Bland–Altman plots of the first and second measurements by the two-dimensional (2D) device. Scatterplot of Bland–Altman limits for maxillary first premolar as measured by the 2D device (<b>a</b>); maxillary first premolar measurements obtained by the 2D device (<b>b</b>); scatterplot of Bland–Altman limits for maxillary first molar as measured by the 2D device (<b>c</b>); maxillary first molar measurements obtained by the 2D device (<b>d</b>); scatterplot of Bland–Altman limits for mandibular first premolar as measured by the SRD (<b>e</b>); mandibular first premolar measurements obtained by the 2D device (<b>f</b>); scatterplot of Bland–Altman limits for mandibular first premolar as measured by the 2D device (<b>g</b>); and mandibular first premolar measurements obtained by the 2D device (<b>h</b>).</p>
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<p>Bland–Altman plots of the first and second measurements by the two-dimensional (2D) device. Scatterplot of Bland–Altman limits for maxillary first premolar as measured by the 2D device (<b>a</b>); maxillary first premolar measurements obtained by the 2D device (<b>b</b>); scatterplot of Bland–Altman limits for maxillary first molar as measured by the 2D device (<b>c</b>); maxillary first molar measurements obtained by the 2D device (<b>d</b>); scatterplot of Bland–Altman limits for mandibular first premolar as measured by the SRD (<b>e</b>); mandibular first premolar measurements obtained by the 2D device (<b>f</b>); scatterplot of Bland–Altman limits for mandibular first premolar as measured by the 2D device (<b>g</b>); and mandibular first premolar measurements obtained by the 2D device (<b>h</b>).</p>
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16 pages, 9844 KiB  
Article
Near-Eye Holographic 3D Display and Advanced Amplitude-Modulating Encoding Scheme for Extended Reality
by Hyoung Lee, Wookho Son, Minseok Kim, Yongjin Yoon and MinSung Yoon
Appl. Sci. 2023, 13(6), 3730; https://doi.org/10.3390/app13063730 - 15 Mar 2023
Cited by 1 | Viewed by 2012
Abstract
Electronic holographic displays can reconstruct the optical wavefront of object light, exhibiting the most realistic three-dimensional (3D) images, in contrast to conventional stereoscopic displays. In this paper, we propose a novel, near-eye holographic 3D display (NEHD) applicable to AR/MR/holographic devices and experimentally demonstrate [...] Read more.
Electronic holographic displays can reconstruct the optical wavefront of object light, exhibiting the most realistic three-dimensional (3D) images, in contrast to conventional stereoscopic displays. In this paper, we propose a novel, near-eye holographic 3D display (NEHD) applicable to AR/MR/holographic devices and experimentally demonstrate the proposed module’s performance with 360° full-viewed holographic 3D movie at 30 fps. To realize high-quality of reconstructed holographic 3D (H3D) images, we also propose an advanced amplitude-modulating (AM) encoding scheme suited for the proposed amplitude-modulating NEHD. We experimentally verify that the new hologram-encoding approach can improve the image quality of H3D reconstructions through quantitative statistical analyses, by using evaluation methods for H3D images that are suggested in the paper. Two holograms at different viewing directions of the same 3D scene are designed to be displayed onto the proposed NEHD prototype for two eyes of an observer, respectively. The presented techniques for the proposed NEHD enable the observer to experience the depth cue, a realistic accommodation effect, and high-quality H3D movies at each eye. Full article
(This article belongs to the Section Computing and Artificial Intelligence)
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<p>(<b>a</b>) Block diagram that shows the image data processing for dual-view holographic content (CGH). (<b>b</b>) Mockup prepared to show the module of dual-view near-eye holographic 3D display (NEHD).</p>
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<p>(<b>a</b>) Schematic of the dual-view near-eye holographic 3D display (NEHD). (<b>b</b>) Prototype setup to demonstrate the proposed NEHD system.</p>
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<p>Geometry for extracting RGB and depth map information and examples of RGB and depth map images: geometry in a perspective view (<b>a</b>) and in a camera-lens view (<b>b</b>). (<b>c</b>,<b>d</b>) are a set for the left eye, and (<b>e</b>,<b>f</b>) are for the right eye. Each image of FHD resolution is extracted at a given view from a virtual camera set to capture the RGB color and depth map images.</p>
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<p>Geometry of the optical system for each eye in the near-eye holographic 3D display. The eye of the observer lens on the <span class="html-italic">u</span>-<span class="html-italic">v</span> coordinate is located at the position of the focal length of the field lens, corresponding to the center of the Fourier plane of the holographic display.</p>
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<p>Four kinds of encoded holograms with FHD resolution. Encoding methods to be used are CAO encoding (<b>a</b>), MAO encoding (<b>b</b>), Burckhardt’s encoding (<b>c</b>), and Lee’s encoding (<b>d</b>).</p>
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<p>Process to prepare CGH content of a frame fit for the dual-view holographic display made up of two AM SLMs.</p>
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<p>Numerical reconstructions using holograms in the case of CAO encoding (<b>a</b>), MAO encoding (<b>b</b>), Burckhardt’s encoding (<b>c</b>), and Lee’s encoding (<b>d</b>) where the left eye of the observer is focused on the front-positioned 3D cube. The object focused is indicated by a blue arrow mark.</p>
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<p>Numerical observations using holograms in the case of CAO encoding (<b>a</b>), MAO encoding (<b>b</b>), Burckhardt’s encoding (<b>c</b>), and Lee’s encoding (<b>d</b>) where the left eye of the observer is focused on the back-positioned 3D cube. The object focused is indicated by a blue arrow mark.</p>
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<p>Experimental optical observations reconstructed from each hologram in the case of CAO encoding (<b>a</b>), MAO encoding (<b>b</b>), Burckhardt’s encoding (<b>c</b>), and Lee’s encoding (<b>d</b>) where the eye of the observer is focused on the fore-positioned object. Each arrow indicates the focused object between a pair of 3D objects in a camera-captured image from optically reconstructed H3D scenes (716 × 450 pixels). Here, each scanned area (27 × 38 pixels) appears in the shape of multiple lines in white color near the front-positioned 3D cube [see also the Supplementary video (a)~(d)].</p>
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<p>Experimental optical observations reconstructed from each hologram in the case of CAO encoding (<b>a</b>), MAO encoding (<b>b</b>), Burckhardt’s encoding (<b>c</b>), and Lee’s encoding (<b>d</b>) where the left eye of the observer is focused on the back-positioned 3D cube that each arrow indicates.</p>
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<p>Surface intensity map of the bright area to be scanned on the optically reconstructed H3D object according to each encoding; (<b>a</b>) CAO encoding, (<b>b</b>) MAO encoding, (<b>c</b>) Burckhardt’s encoding (BUR), and (<b>d</b>) Lee’s encoding (LEE).</p>
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<p>Intensity measurement of (<b>a</b>) bright area and of (<b>b</b>) dark area in H3D images optically reconstructed using four kinds of encodings.</p>
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<p>Diagram to plot holographic contrast ratio (HCR) and brightness analyzed from each scanned bright area on images of optical H3D reconstructions with respect to four different encodings. Blue color of the left vertical axis corresponds to data for contrast ratio, and red color of the right vertical axis corresponds to data for brightness.</p>
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19 pages, 2137 KiB  
Article
The Effect of 3D TVs on Eye Movement and Motor Performance
by Chiuhsiang Joe Lin, Retno Widyaningrum and Yogi Tri Prasetyo
Appl. Sci. 2023, 13(4), 2656; https://doi.org/10.3390/app13042656 - 18 Feb 2023
Cited by 2 | Viewed by 2096
Abstract
Three-dimensional TVs have been commercialized in recent few years; however, poor visual and motor performances may have an impact on consumer acceptance of 3D TVs. The purpose of this study was to investigate the effects of 3D TVs on eye movement and motor [...] Read more.
Three-dimensional TVs have been commercialized in recent few years; however, poor visual and motor performances may have an impact on consumer acceptance of 3D TVs. The purpose of this study was to investigate the effects of 3D TVs on eye movement and motor performance. Specifically, the effect of stereoscopic display parallax of 3D TVs and movement task index of difficulty (ID) on eye movement was investigated. In addition, the effect of stereoscopic display parallax of 3D TVs and movement task ID on motor performance was also investigated. Twelve participants voluntarily participated in a multi-directional tapping task under two different viewing environments (2D TV and 3D TV), three different levels of stereoscopic depth (140, 190, 210 cm), and six different Index of Difficulty levels (2.8, 3.3, 3.7, 4.2, 5.1, 6.1 bit). The study revealed that environment had significant effects on eye movement time, index of eye performance, eye fixation accuracy, number of fixations, time to first fixation, saccadic duration, revisited fixation duration, hand movement time, index of hand performance, and error rate. Interestingly, there were no significant effects of stereoscopic depth on eye movement and motor performance; however, the best performance was found when the 3D object was placed at 210 cm. The main novelty and contributions of this study is the in-depth investigations of the effect of 3D TVs on eye movement and motor performance. The findings of this study could lead to a better understanding of the visual and motor performance for 3D TVs. Full article
(This article belongs to the Special Issue Eye-Tracking Technologies: Theory, Methods and Applications)
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<p>An illustration of the current study.</p>
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<p>The illustration of two different environments. (<b>A</b>) Participant performed tapping task in a 2D environment. (<b>B</b>) Participant utilized 3D glasses to perform the multi-directional tapping task in a 3D environment. 3D TVs were integrated with NVIDIA to create a stereoscopic viewing environment.</p>
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<p>An illustration of the horizontal separation of two images on a 3D TV with three different levels of depth [<a href="#B5-applsci-13-02656" class="html-bibr">5</a>].</p>
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<p>Participant performed the experiment in a 2D environment with distances of (<b>A</b>) 210 cm, (<b>B</b>) 190 cm, and (<b>C</b>) 140 cm.</p>
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<p>The pointing sequence of virtual red balls during multidirectional tapping task (shown as ball 1) [<a href="#B8-applsci-13-02656" class="html-bibr">8</a>].</p>
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9 pages, 2623 KiB  
Article
Assessment of 3D Visual Discomfort Based on Dynamic Functional Connectivity Analysis with HMM in EEG
by Zhiying Long, Lu Liu, Xuefeng Yuan, Yawen Zheng, Yantong Niu and Li Yao
Brain Sci. 2022, 12(7), 937; https://doi.org/10.3390/brainsci12070937 - 18 Jul 2022
Cited by 2 | Viewed by 2214
Abstract
Stereoscopic displays can induce visual discomfort despite their wide application. Electroencephalography (EEG) technology has been applied to assess 3D visual discomfort, because it can capture brain activities with high temporal resolution. Previous studies explored the frequency and temporal features relevant to visual discomfort [...] Read more.
Stereoscopic displays can induce visual discomfort despite their wide application. Electroencephalography (EEG) technology has been applied to assess 3D visual discomfort, because it can capture brain activities with high temporal resolution. Previous studies explored the frequency and temporal features relevant to visual discomfort in EEG data. Recently, it was demonstrated that functional connectivity between brain regions fluctuates with time. However, the relationship between 3D visual discomfort and dynamic functional connectivity (DFC) remains unknown. Although HMM showed advantages over the sliding window method in capturing the temporal fluctuations of DFC at a single time point in functional magnetic resonance imaging (fMRI) data, it is unclear whether HMM works well in revealing the time-varying functional connectivity of EEG data. In this study, the hidden Markov model (HMM) was introduced to DFC analysis of EEG data for the first time and was used to investigate the DFC features that can be used to assess 3D visual discomfort. The results indicated that state 2, with strong connections between electrodes, occurred more frequently in the early period, whereas state 4, with overall weak connections between electrodes, occurred more frequently in the late period for both visual comfort and discomfort stimuli. Moreover, the 3D visual discomfort stimuli caused subjects to stay in state 4 more frequently, especially in the later period, in contrast to the 3D visual comfort stimuli. The results suggest that the increasing occurrence of state 4 was possibly related to visual discomfort and that the occurrence frequency of state 4 may be used to assess visual discomfort. Full article
(This article belongs to the Special Issue Advances in EEG Brain Dynamics)
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<p>The spatial distribution of the selected electrodes. (<b>A</b>–<b>E</b>) The topographic map of the ERP components showing significant differences between visual comfort and discomfort. (<b>F</b>–<b>J</b>) The EPR waveforms of the selected electrodes.</p>
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<p>FC patterns of the four brain states for the visual comfort and discomfort conditions.</p>
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<p>The temporal property comparisons of DFC: (<b>A</b>) comparison of the fraction of time between the visual comfort and discomfort conditions; (<b>B</b>) the probability variations of state 2 and state 4 with the time points for the visual comfort condition; (<b>C</b>) the probability variations of state 2 and state 4 with the time points for the visual discomfort condition. The star * represents <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Results of the simple effects of the period (<b>A</b>,<b>B</b>), state (<b>C</b>,<b>D</b>), and condition (<b>E</b>,<b>F</b>). The star * represents <span class="html-italic">p</span> &lt; 0.05.</p>
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17 pages, 11375 KiB  
Article
Autostereoscopic 3D Display System for 3D Medical Images
by Dongwoo Kang, Jin-Ho Choi and Hyoseok Hwang
Appl. Sci. 2022, 12(9), 4288; https://doi.org/10.3390/app12094288 - 24 Apr 2022
Cited by 9 | Viewed by 6687
Abstract
Recent advances in autostereoscopic three-dimensional (3D) display systems have led to innovations in consumer electronics and vehicle systems (e.g., head-up displays). However, medical images with stereoscopic depth provided by 3D displays have yet to be developed sufficiently for widespread adoption in diagnostics. Indeed, [...] Read more.
Recent advances in autostereoscopic three-dimensional (3D) display systems have led to innovations in consumer electronics and vehicle systems (e.g., head-up displays). However, medical images with stereoscopic depth provided by 3D displays have yet to be developed sufficiently for widespread adoption in diagnostics. Indeed, many stereoscopic 3D displays necessitate special 3D glasses that are unsuitable for clinical environments. This paper proposes a novel glasses-free 3D autostereoscopic display system based on an eye tracking algorithm and explores its viability as a 3D navigator for cardiac computed tomography (CT) images. The proposed method uses a slit-barrier with a backlight unit, which is combined with an eye tracking method that exploits multiple machine learning techniques to display 3D images. To obtain high-quality 3D images with minimal crosstalk, the light field 3D directional subpixel rendering method combined with the eye tracking module is applied using a user’s 3D eye positions. Three-dimensional coronary CT angiography images were volume rendered to investigate the performance of the autostereoscopic 3D display systems. The proposed system was trialed by expert readers, who identified key artery structures faster than with a conventional two-dimensional display without reporting any discomfort or 3D fatigue. With the proposed autostereoscopic 3D display systems, the 3D medical image navigator system has the potential to facilitate faster diagnoses with improved accuracy. Full article
(This article belongs to the Collection Virtual and Augmented Reality Systems)
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<p>(<b>a</b>) Eye tracking-based light-field 3D display concept, showing the generation and modeling of directional light rays. (<b>b</b>) 3D display prototype for medical applications: 3D cardiac CT navigator.</p>
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<p>Autostereoscopic 3D display concept: (1) Directional light is generated by liquid crystal display (LCD) panels; (2) the 3D light field is modeled; and (3) 3D light rendering is performed according to user eye position, as determined by eye tracking algorithms.</p>
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<p>Parameterization of a light ray in the 3D display environment.</p>
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<p>A horizontal 2D slice of light field in the 3D display. (<b>a</b>) Real-world setting, (<b>b</b>) equivalent representation in the ray space. Greyscale lines represent light rays passing through slits (lens), red and blue lines represent light rays passing through the left and right eye respectively. Square boxes are intersected points.</p>
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<p>Illustration of the proposed eye tracking method, which includes pupil segmentation modules. The left image shows an extracted subregion (red box) including the eyes and nose of the subject. The middle image shows the 11 landmark points (green dots) used to inform the Supervised Descent Method (SDM)-based shape alignments. In the right image, the green circles around the red points on the eyes indicate the pupil segmentation modules, which increase the accuracy of the eye tracking.</p>
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<p>Eye center positions are refined with our proposed re-weight algorithm for eye occluded faces. From the MobileNet v2 based PFLD method (the upper row), the re-weight subnet infers the pixel confidence on the feature map from both the structure between landmarks and landmark appearance as shown in the bottom row.</p>
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<p>Three-dimensional cardiac CT navigator software prototype. The images show the volume rendered original CT images without any pre-processing (<b>1st row</b>), a segmented whole heart (<b>2nd row</b>), and segmented coronary arteries (<b>3rd row</b>).</p>
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<p>Three-dimensional cardiac CT navigator usage examples. The 3D display serves as a 3D navigator for identifying coronary lesion candidates within complex coronary artery structures. The candidates can then be examined in detail using 2D modes with 2D cardiac CT images.</p>
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<p>Proposed implemented prototypes of an autostereoscopic 3D display system for 3D medical images ((<b>left</b>) 31.5”, (<b>middle</b>) 18.4”, (<b>right</b>) 10.1”). In the 31.5” monitory prototype (<b>left</b>), the light field 3D subpixel rendering is processed with a GPU and the eye tracking algorithm is processed with only CPU computations in Windows PC. In the 18.4” (<b>middle</b>) and the 10.1” (<b>right</b>) tablet display prototypes, the light field 3D subpixel rendering and eye tracking are processed in the FPGA board.</p>
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<p>Examples of the two-view image captured at different distances: (<b>a</b>) captured images of rendering without eye positions, (<b>b</b>) captured images using the proposed 3D light field rendering method with eye positions.</p>
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<p>Illustration of the proposed eye tracking method with pupil segmentation modules. The green circles around the red points on the eyes indicate the pupil segmentation modules, which increase the accuracy of the eye tracking. The left side shows the left camera image from a stereo webcam, while the right side shows the image captured by the right camera. The left and right images were combined to calculate the 3D eye position via stereo image matching based on triangular interpolation.</p>
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<p>Stereo images from the volume rendered 3D coronary arteries from a coronary CT angiography image dataset. Left view image (<b>left</b>) and right view image (<b>right</b>).</p>
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<p>Unclear left- and right-view separation resulting from failed eye tracking. The resulting 3D crosstalk manifests as overlapping double images and 3D fatigue for the user.</p>
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<p>Example of the augmented morphological information provided by the proposed 3D display system. The 1st row shows the left and right views from the 3D display prototype. The 2nd row shows an enlarged region of interest (red box, 1st row), and clearly highlights the improvement provided by the proposed system with respect to deciphering the morphology of coronary arteries: the left image shows separate arteries, whereas the right image shows overlapping information. The user receives both images to both eyes; therefore, they experience enhanced 3D depth perception and access more morphological information compared to 2D displays.</p>
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<p>Examples of the proposed 3D display system being applied to other modalities; namely, abdomen CT and head CT. The inset camera images show the real-time eye tracking results for the proposed autostereoscopic 3D display.</p>
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13 pages, 4808 KiB  
Article
Comparison of the Observation Errors of Augmented and Spatial Reality Systems
by Masataka Ariwa, Tomoki Itamiya, So Koizumi and Tetsutaro Yamaguchi
Appl. Sci. 2021, 11(24), 12076; https://doi.org/10.3390/app112412076 - 18 Dec 2021
Cited by 3 | Viewed by 2849
Abstract
Using 3D technologies such as virtual reality (VR) and augmented reality (AR), has intensified nowadays. The mainstream AR devices in use today are head-mounted displays (HMDs), which, due to specification limitations, may not perform to their full potential within a distance of 1.0 [...] Read more.
Using 3D technologies such as virtual reality (VR) and augmented reality (AR), has intensified nowadays. The mainstream AR devices in use today are head-mounted displays (HMDs), which, due to specification limitations, may not perform to their full potential within a distance of 1.0 m. The spatial reality display (SRD) is another system that facilitates stereoscopic vision by the naked eye. The recommended working distance is 30.0~75.0 cm. It is crucial to evaluate the observation accuracy within 1.0 m for each device in the medical context. Here, 3D-CG models were created from dental models, and the observation errors of 3D-CG models displayed within 1.0 m by HMD and SRD were verified. The measurement error results showed that the HMD model yielded more significant results than the control model (Model) under some conditions, while the SRD model had the same measurement accuracy as the Model. The measured errors were 0.29~1.92 mm for HMD and 0.02~0.59 mm for SRD. The visual analog scale scores for distinctness were significantly higher for SRD than for HMD. Three-dimensionality did not show any relationship with measurement error. In conclusion, there is a specification limitation for using HMDs within 1.0 m, as shown by the measured values. In the future, it will be essential to consider the characteristics of each device in selecting the use of AR devices. Here, we evaluated the accuracies of 3D-CG models displayed in space using two different systems of AR devices. Full article
(This article belongs to the Collection Virtual and Augmented Reality Systems)
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<p>The positional relationship between the eyes, 3D-CG model, an augmented reality (AR) device, and objects (e.g., calipers). In the head-mounted display (HMD), the yellow area is in front of the 3D-CG model, and overlapping objects (e.g., calipers) are displayed. For the spatial reality display (SRD), the blue area is behind the 3D-CG model, and it is displayed without overlapping the object (e.g., calipers).</p>
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<p>Standard model with a mark on the measurement cusp.</p>
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<p>Digital model with a mark on the measurement cusp.</p>
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<p>3D-CG model displayed on HMD.</p>
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<p>3D-CG model displayed on SRD.</p>
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<p>Measurement in the horizontal direction.</p>
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<p>Measurement in the direction of the depth.</p>
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<p>The positional relationship between the person taking the measurement and the measured object at SP.</p>
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<p>The positional relationship between the person taking the measurement and the measured object in the StP.</p>
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<p>3D-CG model at the time of measurement by HMD.</p>
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<p>3D-CG model at the time of measurement by SRD.</p>
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<p>Bland–Altman analysis of first and second measurements. Scatter plots of Bland–Altman limits for SP + Model + Horizontal (<b>a</b>). Bland–Altman plot of SP + Model + Horizontal measurements (<b>b</b>). Scatter plots of Bland–Altman limits for SP + HMD + Horizontal (<b>c</b>). Bland–Altman plot of SP + HMD + Horizontal measurements (<b>d</b>). Scatter plots of Bland–Altman limits for SP + SRD + Horizontal (<b>e</b>). Bland–Altman plot of SP + SRD + Horizontal measurements (<b>f</b>).</p>
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<p>Scatter plot of the Bland–Altman match limits for the measurements by condition. SP + HMD + Horizontal (<b>a</b>). SP + SRD + Horizontal (<b>b</b>). SP + HMD + Depth (<b>c</b>). SP + SRD + Depth (<b>d</b>). StP + HMD + Horizontal (<b>e</b>). StP + SRD + Horizontal (<b>f</b>). StP + HMD + Depth (<b>g</b>) and StP + SRD + Depth (<b>h</b>).</p>
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<p>Bland–Altman analysis of the measurements by condition. SP + HMD + Horizontal measurements (<b>a</b>). SP + SRD + Horizontal measurements (<b>b</b>). SP + HMD + Depth measurements (c). SP + SRD + Depth measurements (<b>d</b>). StP + HMD + Horizontal measurements (<b>e</b>). StP + SRD + Horizontal measurements (<b>f</b>). StP + HMD + Depth measurements (<b>g</b>) and StP + SRD + Depth measurements (<b>h</b>).</p>
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<p>Three-dimensionality and distinctness of HMD and SRD compared with those of the Model.</p>
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18 pages, 3219 KiB  
Article
A System for Real-Time, Online Mixed-Reality Visualization of Cardiac Magnetic Resonance Images
by Dominique Franson, Andrew Dupuis, Vikas Gulani, Mark Griswold and Nicole Seiberlich
J. Imaging 2021, 7(12), 274; https://doi.org/10.3390/jimaging7120274 - 14 Dec 2021
Cited by 6 | Viewed by 3655
Abstract
Image-guided cardiovascular interventions are rapidly evolving procedures that necessitate imaging systems capable of rapid data acquisition and low-latency image reconstruction and visualization. Compared to alternative modalities, Magnetic Resonance Imaging (MRI) is attractive for guidance in complex interventional settings thanks to excellent soft tissue [...] Read more.
Image-guided cardiovascular interventions are rapidly evolving procedures that necessitate imaging systems capable of rapid data acquisition and low-latency image reconstruction and visualization. Compared to alternative modalities, Magnetic Resonance Imaging (MRI) is attractive for guidance in complex interventional settings thanks to excellent soft tissue contrast and large fields-of-view without exposure to ionizing radiation. However, most clinically deployed MRI sequences and visualization pipelines exhibit poor latency characteristics, and spatial integration of complex anatomy and device orientation can be challenging on conventional 2D displays. This work demonstrates a proof-of-concept system linking real-time cardiac MR image acquisition, online low-latency reconstruction, and a stereoscopic display to support further development in real-time MR-guided intervention. Data are acquired using an undersampled, radial trajectory and reconstructed via parallelized through-time radial generalized autocalibrating partially parallel acquisition (GRAPPA) implemented on graphics processing units. Images are rendered for display in a stereoscopic mixed-reality head-mounted display. The system is successfully tested by imaging standard cardiac views in healthy volunteers. Datasets comprised of one slice (46 ms), two slices (92 ms), and three slices (138 ms) are collected, with the acquisition time of each listed in parentheses. Images are displayed with latencies of 42 ms/frame or less for all three conditions. Volumetric data are acquired at one volume per heartbeat with acquisition times of 467 ms and 588 ms when 8 and 12 partitions are acquired, respectively. Volumes are displayed with a latency of 286 ms or less. The faster-than-acquisition latencies for both planar and volumetric display enable real-time 3D visualization of the heart. Full article
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<p>Concept illustration of the real-time, online mixed-reality visualization system. MR data are collected using an undersampled, radial trajectory for rapid acquisition. Real-time image reconstruction is performed using a parallelized implementation of through-time radial GRAPPA. The final images are rendered in a mixed-reality headset for intuitive display.</p>
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<p>Schematic of data flow within the online acquisition, reconstruction, and mixed-reality rendering system. A dedicated image reconstruction computer was introduced into the network containing the scanner, the scanner control computer, and the scanner measurement computer via a switch and an Ethernet cable. A second network was set up using a router to contain the reconstruction computer and the mixed-reality headset. Two configurations were possible. In the first, reconstructed data are sent directly to a self-contained HMD capable of handling the entire rendering pipeline. In the second, a dedicated rendering workstation is used. The reconstruction and rendering computers are connected via Ethernet cables, and rendered frame data are transferred wirelessly to the headset. Solid gray lines show component connections, and dashed red lines depict data transfer.</p>
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<p>Schematic showing the relative timings of the acquisition, reconstruction, and rendering processes and their sub-steps for a two-slice planar scan. Each frame comprises two slices that are acquired sequentially. The removal of readout oversampling is performed on each line of data as it is acquired, as indicated by the diagonal bar across the sub-step rectangle. Data for the slice are buffered, followed by coil compression, GRAPPA, the NUFFT, and export of the images to the rendering system. The rendering processing includes receiving the image data over the TCP socket, parsing the data, and processing the data. Once all slices per frame are processed, as detected by an update flag, the images are rendered to the user on the next headset frame update. The net display latency is considered to be the time between when all of the data for one frame have been acquired and when that frame is displayed to the user. Note that all components of the system may be active at once, operating on different slices and/or frames of data concurrently.</p>
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<p>Schematic showing the time required for multiple frames of data to pass through the system for three different dataset types: (<b>top</b>) single-slice planar, (<b>middle</b>) two-slice planar, and (<b>bottom</b>) eight-partition volumetric. Planar data are acquired continuously, while volumetric data are ECG-gated. Images are displayed to the user before completing acquisition of the following frame for all cases. Different colors represent different frames.</p>
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<p>Operation of the full system at the MR scanner. (<b>Left</b>) A user wearing a mixed-reality headset sits at the scanner control computer before scanning a healthy volunteer lying in the scanner bore (red dashed circle). (<b>Middle</b>) During the scan, the user sees a multi-slice rendering of the volunteer’s heart in real-time. (<b>Right</b>) User’s view of the rendering. Note that what appears black in the image appears transparent to the user; the user sees the rendering within the natural environment. A video version of this figure is available in the <a href="#app1-jimaging-07-00274" class="html-app">Supplementary Materials</a>.</p>
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<p>Multiple still frames from a user’s perspective of a volumetric scan through a mixed-reality headset are shown. The user moved around the rendering, changed the viewing angle, and moved toward and away from it. The stills were captured by a program in the headset that combines the rendering that is projected to the user with a video capture from the camera embedded in the headset. A video version of this figure is available in the <a href="#app1-jimaging-07-00274" class="html-app">Supplementary Materials</a>.</p>
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<p>Example 2D panel display and corresponding renderings. (<b>Top left</b>) Three slices in short-axis and four-chamber views. Note that the dark bands across the ventricles in the four-chamber view are from saturation of the signal where the short-axis slices intersect. (<b>Top right</b>) Rendering of the slices in the correct spatial positions and orientations. (<b>Bottom left</b>) Panel display of eight partitions in a volumetric dataset centered over the left ventricle. (<b>Bottom right</b>) Rendering of the dataset.</p>
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14 pages, 2521 KiB  
Article
A Novel Anatomy Education Method Using a Spatial Reality Display Capable of Stereoscopic Imaging with the Naked Eye
by Tomoki Itamiya, Masahiro To, Takeshi Oguchi, Shinya Fuchida, Masato Matsuo, Iwao Hasegawa, Hiromasa Kawana and Katsuhiko Kimoto
Appl. Sci. 2021, 11(16), 7323; https://doi.org/10.3390/app11167323 - 9 Aug 2021
Cited by 11 | Viewed by 4169
Abstract
Several efforts have been made to use virtual reality (VR) and augmented reality (AR) for medical and dental education and surgical support. The current methods still require users to wear devices such as a head-mounted display (HMD) and smart glasses, which pose challenges [...] Read more.
Several efforts have been made to use virtual reality (VR) and augmented reality (AR) for medical and dental education and surgical support. The current methods still require users to wear devices such as a head-mounted display (HMD) and smart glasses, which pose challenges in hygiene management and long-term use. Additionally, it is necessary to measure the user’s inter-pupillary distance and to reflect it in the device settings each time to accurately display 3D images. This setting is difficult for daily use. We developed and implemented a novel anatomy education method using a spatial reality display capable of stereoscopic viewing with the naked eye without an HMD or smart glasses. In this study, we developed two new applications: (1) a head and neck anatomy education application, which can display 3D-CG models of the skeleton and blood vessels of the head and neck region using 3D human body data available free of charge from public research institutes, and (2) a DICOM image autostereoscopic viewer, which can automatically convert 2D CT/MRI/CBCT image data into 3D-CG models. In total, 104 students at the School of Dentistry experienced and evaluated the system, and the results suggest its usefulness. A stereoscopic display without a head-mounted display is highly useful and promising for anatomy education. Full article
(This article belongs to the Collection Virtual and Augmented Reality Systems)
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<p>The SRD and a user pointing to the 3D-CG model.</p>
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<p>One example of SR Anatomy.</p>
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<p>Another example of SR Anatomy.</p>
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<p>One example of finger manipulation.</p>
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<p>Another example of finger manipulation.</p>
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<p>Example of DSR View (CT).</p>
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<p>Example of DSR View (CBCT).</p>
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<p>An example of a dental student experiencing the SRD.</p>
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<p>Another example of a dental student experiencing the SRD.</p>
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<p>Impressions of the SRD experience.</p>
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<p>Comprehension of anatomy.</p>
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<p>Necessity of the application.</p>
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<p>Necessity of the application.</p>
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<p>Measurement in the horizontal direction.</p>
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<p>Measurement in the horizontal direction.</p>
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<p>Measurement in the depth direction.</p>
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<p>Measurement in the depth direction.</p>
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24 pages, 11933 KiB  
Article
The Design and Development of the Destiny-Class CyberCANOE Hybrid Reality Environment
by Dylan Kobayashi, Ryan Theriot, Noel Kawano, Jack Lam, Eric Wu, Tyson Seto-Mook, Alberto Gonzalez, Ken Uchida, Andrew Guagliardo, Kari Noe and Jason Leigh
Electronics 2021, 10(4), 513; https://doi.org/10.3390/electronics10040513 - 22 Feb 2021
Viewed by 2683
Abstract
The Destiny-class CyberCANOE (Destiny) is a Hybrid Reality environment that provides 20/20 visual acuity in a 13-foot-wide, 320-degree cylindrical structure comprised of tiled passive stereo-capable organic light emitting diode (OLED) displays. Hybrid Reality systems combine surround-screen virtual reality environments with ultra-high-resolution digital project-rooms. [...] Read more.
The Destiny-class CyberCANOE (Destiny) is a Hybrid Reality environment that provides 20/20 visual acuity in a 13-foot-wide, 320-degree cylindrical structure comprised of tiled passive stereo-capable organic light emitting diode (OLED) displays. Hybrid Reality systems combine surround-screen virtual reality environments with ultra-high-resolution digital project-rooms. They are intended as collaborative environments that enable multiple users to work minimally encumbered for long periods of time in rooms surrounded by data in the form of visualizations that benefit from being displayed at resolutions matching visual acuity and/or in stereoscopic 3D. Destiny is unique in that it is the first Hybrid Reality system to use OLED displays and it uses a real-time GPU-based approach for minimizing stereoscopic crosstalk. This paper chronicles the non-trivial engineering research and attention-to-detail that is required to develop a production quality hybrid-reality environment by providing details about Destiny’s design and construction process. This detailed account of how a Hybrid Reality system is designed and constructed from the ground up will help VR researchers and developers understand the engineering complexity of developing such systems. This paper also discusses a GPU-based crosstalk mitigation technique and evaluation, and the use of Microsoft’s augmented reality headset, the HoloLens, as a design and training aid during construction. Full article
(This article belongs to the Special Issue Recent Advances in Virtual Reality and Augmented Reality)
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<p>External view of the Destiny-class CyberCANOE.</p>
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<p>The Destiny-class CyberCANOE showing a visualization of coral reef data from the Hawai’i Institute of Marine Biology (data courtesy of John Burns).</p>
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<p>Top-down view of a portrait-oriented display with 3D 20-degree FOV lines for the left-most and right-most pixel. Minimum 3D viewing distance occurs at the intersection of the two dotted lines.</p>
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<p>Wireframe design of Destiny in AutoCAD.</p>
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<p>Hologram of the Destiny model as seen through a HoloLens to evaluate its fit within the room’s constraints.</p>
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<p>A student constructing a column of Destiny utilizing the HoloLens.</p>
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<p>Step 6 of the Destiny construction manual depicted in the HoloLens on the left, and as a traditional orthographic drawing on the right.</p>
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<p>The 3D printed spacer for supporting the displays.</p>
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<p>Design of the 3D printed model in AutoDesk Inventor.</p>
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<p>To the left is the dreamGEAR PS3 Move Equalizer shotgun holster. On the right are the Destiny controllers which have replaced the barrel portion with a 3D printed tracking constellation.</p>
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<p>A student using the wands and head tracker in Destiny. A student wearing the three tracked devices (2 wands and head).</p>
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<p>Destiny’s control interface.</p>
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<p>The glyphs will light up as nodes check into the control computer.</p>
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<p>From the main page, all blue buttons represent applications that can be launched (<b>A</b>). After pressing the Point Clouds button, a loading page will display (<b>B</b>). Once Loaded, some information about the application will be presented and show the button to stop the app (<b>C</b>). To stop the app, first press the Stop App button, to reveal a Confirm button. Pressing the Confirm button will stop the app and return to the main screen (<b>D</b>).</p>
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<p>On the bottom of the main page is the Admin button (<b>A</b>). The admin page presents additional options for launching and sending messages (<b>B</b>). Pressing the File List button will show a list of all apps that have been uploaded (<b>C</b>). Clicking on any of them will swap to the loading screen (<b>D</b>).</p>
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<p>To power on Destiny, the following process is repeated for each node. (1) First, the control computer sends a power on signal to each of that node’s displays service port over RS-232. (2) Second, a Wake-On-LAN is sent for that node over Ethernet. (3) Node.js will launch as part of Windows startup, when it will check in with the control computer over WebSocket.</p>
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<p>To launch an application, the control computer sends a message over WebSocket to each node. After receiving the node, they will start the app launching .bat file and pass in the given parameters along with their own node number.</p>
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<p>To update the control system, first the command is initiated through the admin page. After the control computer receives the command, it notifies all other nodes, activates the update .bat file, then shuts itself down. The activated .bat file spawns a separate Command Prompt window to run the real update .bat file. That Command Prompt stays active even after Node.js shuts down. A Git pull is performed from the online repo, and after the pull restarts Nodejs.</p>
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<p>3D model of Destiny showing four displays (<b>A</b>–<b>D</b>), which are powered by one computer.</p>
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<p>Controls for manipulating the simulation.</p>
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<p>The API provides highlighting within Unity to show Destiny’s orientation within the scene.</p>
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<p>Off-axis crosstalk.</p>
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<p>Perpendicular and Off-Center Distance.</p>
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<p>Left and right starting pixel determination.</p>
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<p>Crosstalk minimum and maximum within fragment shader.</p>
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<p>Top-down view of Destiny and the positions used for each evaluation.</p>
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<p>Geometric Shapes Scene.</p>
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<p>Jungle Scene.</p>
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<p>Comparison of the two anti-crosstalk approaches (F1 and F2) under two different scenes (Shapes Scene and Jungle Scene).</p>
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