Atapour-Abarghouei et al., 2019 - Google Patents
Generative adversarial framework for depth filling via wasserstein metric, cosine transform and domain transferAtapour-Abarghouei et al., 2019
- Document ID
- 7033310952557858652
- Author
- Atapour-Abarghouei A
- Akcay S
- de La Garanderie G
- Breckon T
- Publication year
- Publication venue
- Pattern Recognition
External Links
Snippet
In this work, the issue of depth filling is addressed using a self-supervised feature learning model that predicts missing depth pixel values based on the context and structure of the scene. A fully-convolutional generative model is conditioned on the available depth …
- 238000011049 filling 0 title abstract description 36
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10024—Color image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30781—Information retrieval; Database structures therefor; File system structures therefor of video data
- G06F17/30784—Information retrieval; Database structures therefor; File system structures therefor of video data using features automatically derived from the video content, e.g. descriptors, fingerprints, signatures, genre
- G06F17/30799—Information retrieval; Database structures therefor; File system structures therefor of video data using features automatically derived from the video content, e.g. descriptors, fingerprints, signatures, genre using low-level visual features of the video content
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T15/00—3D [Three Dimensional] image rendering
- G06T15/10—Geometric effects
- G06T15/20—Perspective computation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T13/00—Animation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T9/00—Image coding, e.g. from bit-mapped to non bit-mapped
- G06T9/001—Model-based coding, e.g. wire frame
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N13/00—Stereoscopic or multiview television systems; Details thereof
- H04N13/02—Picture signal generators
- H04N13/0203—Picture signal generators using a stereoscopic image camera
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Laga et al. | A survey on deep learning techniques for stereo-based depth estimation | |
Gadelha et al. | 3d shape induction from 2d views of multiple objects | |
Atapour-Abarghouei et al. | Generative adversarial framework for depth filling via wasserstein metric, cosine transform and domain transfer | |
Long et al. | Multi-view depth estimation using epipolar spatio-temporal networks | |
Guillemot et al. | Image inpainting: Overview and recent advances | |
Yan et al. | Ddrnet: Depth map denoising and refinement for consumer depth cameras using cascaded cnns | |
Gilbert et al. | Volumetric performance capture from minimal camera viewpoints | |
Sun et al. | Layered RGBD scene flow estimation | |
Wang et al. | Physically guided liquid surface modeling from videos | |
Vitoria et al. | Semantic image inpainting through improved wasserstein generative adversarial networks | |
US20240013479A1 (en) | Methods and Systems for Training Quantized Neural Radiance Field | |
Wang et al. | Depth estimation of video sequences with perceptual losses | |
CN113850900A (en) | Method and system for recovering depth map based on image and geometric clue in three-dimensional reconstruction | |
Yang et al. | Depth map super-resolution using stereo-vision-assisted model | |
Lu et al. | 3D real-time human reconstruction with a single RGBD camera | |
Venkat et al. | Deep textured 3d reconstruction of human bodies | |
Liu et al. | A survey on deep learning methods for scene flow estimation | |
Wang et al. | Splatflow: Learning multi-frame optical flow via splatting | |
Sharma et al. | A novel 3d-unet deep learning framework based on high-dimensional bilateral grid for edge consistent single image depth estimation | |
CN114494395A (en) | Depth map generation method, device and equipment based on plane prior and storage medium | |
Yu et al. | Uv-based 3d hand-object reconstruction with grasp optimization | |
Gong | Eggs: Edge guided gaussian splatting for radiance fields | |
Lee et al. | Automatic 2d-to-3d conversion using multi-scale deep neural network | |
Khan et al. | Towards monocular neural facial depth estimation: Past, present, and future | |
Sheng et al. | High-quality video generation from static structural annotations |