Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

3D Reconstruction of Indoor Scenes via Image Registration

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

With the development of computer vision technologies, 3D reconstruction has become a hotspot. At present, 3D reconstruction relies heavily on expensive equipment and has poor real-time performance. In this paper, we aim at solving the problem of 3D reconstruction of an indoor scene with large vertical span. In this paper, we propose a novel approach for 3D reconstruction of indoor scenes with only a Kinect. Firstly, this method uses a Kinect sensor to get color images and depth images of an indoor scene. Secondly, the combination of scale-invariant feature transform and random sample consensus algorithm is used to determine the transformation matrix of adjacent frames, which can be seen as the initial value of iterative closest point (ICP). Thirdly, we establish the relative coordinate relation between pair-wise frames which are the initial point cloud data by using ICP. Finally, we achieve the 3D visual reconstruction model of indoor scene by the top-down image registration of point cloud data. This approach not only mitigates the sensor perspective restriction and achieves the indoor scene reconstruction of large vertical span, but also develops the fast algorithm of indoor scene reconstruction with large amount of cloud data. The experimental results show that the proposed algorithm has better accuracy, better reconstruction effect, and less running time for point cloud registration. In addition, the proposed method has great potential applied to 3D simultaneous location and mapping.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Barnard ST, Fischler MA (1982) Computational stereo. ACM Comput Surv 14(4):553–572

    Article  Google Scholar 

  2. Thrun S, Burgard W, Fox D (2000) A real-time algorithm for mobile robot mapping with applications to multi-robot and 3D mapping. In: IEEE international conference on robotics and automation, pp 321–328

  3. Huang MQ, Wang ZY, Hao LQ, Zhou LZ (2006) Laser aerosol time-of-flight mass spectrometry analysis of individual aerosol particles from photooxidation of toluene. Opt Appl 36(1):1–5

    Google Scholar 

  4. Filipik A, Jan J, Peterlik I (2012) Time-of-Flight based calibration of an ultrasonic computed tomography system. Radio Eng 23(8):346–355

    Google Scholar 

  5. Zhang Z (2012) Microsoft kinect sensor and its effect. IEEE Multimed 19(2):4–10

    Article  Google Scholar 

  6. Microsoft Kinect. http://www.xbox.com/de-de/kinect

  7. Mair E, Strobl KH, Bodenmüller T, Suppa M, Burschka D (2010) Real-time image-based localization for hand-held 3D-modeling. KI-Künstl Intell 24(3):207–214

    Article  Google Scholar 

  8. Besl PJ, McKay ND (2002) A method for registration of 3-D shapes. In: IEEE transactions on pattern analysis & machine intelligence, vol 14, no 2, pp 239–256

    Article  Google Scholar 

  9. Rusinkiewicz S, Levoy M (2001) Efficient variants of the ICP algorithm. In: IEEE conference on 3D digital imaging and modeling, pp 145–152

  10. Henry P, Krainin M, Herbst E, Ren X, Fox D (2012) RGB-D mapping: using Kinect-style depth cameras for dense 3D modeling of indoor environments. Int J Robot Res 31(5):647–663

    Article  Google Scholar 

  11. Newcombe RA, Izadi S, Hilliges O, Molyneaux D, Kim D, Davison AJ, Kohi P, Shotton J, Hodges S, Fitzgibbon A (2011)KinectFusion: real-time dense surface mapping and tracking. In: IEEE international symposium on mixed and augmented reality, pp 127–136

  12. Izadi S, Kim D, Hilliges O, Molyneaux D, Newcombe R,Kohli P, Shotton J, Hodges S, FreemanD, Davison A (2011) KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th annual ACM symposium on user interface software and technology, pp 559–568

  13. Liu X, Xu HR, Hu ZY (2012) GPU based fast 3D-object modeling with kinect. Acta Autom Sin 38(8):1288–1297

    Article  Google Scholar 

  14. Zou Y, Chen W, Wu X, Liu Z (2012) Indoor localization and 3D scene reconstruction for mobile robots using the Microsoft Kinect sensor. In: IEEE international conference on industrial informatics, pp 1182–1187

  15. Yue H, Chen W, Wu X, Liu J (2014) Fast 3D modeling in complex environments using a single Kinect sensor. Opt Lasers Eng 53(1):104–111

    Article  Google Scholar 

  16. Wang K, Zhang G, Bao H (2014) Robust 3D reconstruction with an RGB-D camera. IEEE Trans Image Process 23(11):4893–4906

    Article  MathSciNet  Google Scholar 

  17. Choi S, Zhou QY, Koltun V (2015) Robust reconstruction of indoor scenes. In: IEEE conference on computer vision and pattern recognition, pp 5556–5565

  18. Mei F, Liu J, Li CP, Wang ZQ (2015) Improved RGB-D camera based indoor scene reconstruction. J Image Graph 20(10):1366–1373

    Google Scholar 

  19. Thomas D, Sugimoto A (2013) A flexible scene representation for 3D reconstruction using an RGB-D camera. In: IEEE international conference on computer vision, pp 2800–2807

  20. Yang Y, Gao M, Yin K, Wu Z (2015) High-quality depth map reconstruction combining stereo image pair. J Image Graph 20(1):1–10

    Google Scholar 

  21. Xiao JX, Owens A, Torralba A (2013) SUN3D: a database of big spaces reconstructed using sfm and object labels. In: IEEE international conference on computer vision, pp 1625–1632

  22. Zhang CH, Zhu RJ, Zhuang Y (2013) Indoor 3D scene reconstruction based on Kinect depth camera. Netw J Grad Sch Dalian Univ Technol 5:1–8

    Google Scholar 

  23. Bay H, Ess A, Tuytelaars T, Gool LV (2008) Speeded-up robust features (SURF). Comput Vis Image Underst 110(3):346–359

    Article  Google Scholar 

  24. Chen XM, Jiang LT, Ying RD (2013) Research of 3D reconstruction and filtering algorithm based on depth information of Kinect. Appl Res Comput 30(4):1216–1218

    Google Scholar 

  25. Izadi S, Stamminger M (2013) Real-time 3D reconstruction at scale using voxel hashing. ACM Trans Graph 32(6):169–187

    Google Scholar 

  26. Liu Z, Zhang Y, Wu W, Liu K, Sun Z (2015) Model-driven indoor scenes modeling from a single image. In: Graphics interface conference canadian information processing society, pp 25–32

  27. Li YF, Zhang GL, Xu J, Yao EL (2016) Improved ICP in frame-to-frame registration based on Kinect. Electron Opt Control 23(2):56–60

    Google Scholar 

  28. Du SY, Liu J, Zhang CJ, Zhu JH, Li K (2015) Probability iterative closest point algorithm for m-D point set registration with noise. Neurocomputing 157:187–198

    Article  Google Scholar 

  29. Du SY, Liu J, Bi B, Zhu JH, Xue JR (2016) New iterative closest point algorithm for isotropic scaling registration of point sets with noise. J Vis Commun Image Represent 38:207–216

    Article  Google Scholar 

  30. Ying S, Wu G, Wang Q, Shen D (2014) Hierarchical unbiased graph shrinkage (HUGS): a novel groupwise registration for large data set. Neuroimage 84(1):626–638

    Article  Google Scholar 

  31. Ying S, Wang Y, Wen Z, Lin Y (2016) Nonlinear 2D shape registration via thin-plate spline and Lie group representation. Neurocomputing 195:129–136

    Article  Google Scholar 

  32. Santos DRD, Basso MA, Khoshelham K, Oliveira ED, Pavan NL, Vosselman G (2016) Mapping indoor spaces by adaptive coarse-to-fine registration of RGB-D data. IEEE Geosci Remote Sens Lett 13(2):262–266

  33. Xi L (2017) A real time implementation of 3D symmetric object reconstruction. Faculty of the Graduate School of the University of Maryland, College Park

    Google Scholar 

  34. Lv Q, Lin H, Wang G, Wei H, Wang Y (2017) ORB-SLAM-based tracing and 3D reconstruction for robot using Kinect 2.0. In: IEEE control and decision conference, pp 3319–3324

  35. Nuchter A, Lingemann K, Hertzberg J (2007) Cached k-d tree search for ICP algorithms. In: IEEE international conference on 3D digital imaging and modeling, pp 419–426

  36. Chen HM, Lin TH (2006) An algorithm to build convex hulls for 3D objects. J Chin Inst Eng 29(6):945–952

    Article  MathSciNet  Google Scholar 

  37. Lowe DG (1999) Object recognition from local scale-invariant features. Int Conf Comput Vis 2:1150–1157

  38. Helmer S, Lowe DG (2004) Object class recognition with many local features. In: IEEE conference on computer vision and pattern recognition workshop, pp 182–187

  39. Lindeberg T (2013) Image matching using generalized scale-space interest points. In: International conference on scale space and variational methods in computer vision, pp 355–367

    Google Scholar 

  40. Xu M, Lu J (2012) Distributed RANSAC for the robust estimation of three-dimensional reconstruction. In: IEEE international conference on computer vision, pp 324–333

  41. Fischler MA, Bolles RC (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24(6):381–395

    Article  MathSciNet  Google Scholar 

  42. Li C, Xue JR, Du SY, Zheng NN (2010) A fast multi-resolution iterative closest point algorithm. In: Chinese conference on pattern recognition, pp 1–5

Download references

Acknowledgements

The paper was supported in part by the National Natural Science Foundation (NSFC) of China under Grant Nos. 61373077, 61365003 and Gansu Province Basic Research Innovation Group Project No. 1506RJIA031.

Author information

Authors and Affiliations

  1. Lanzhou University of Technology, Lanzhou, Gansu Province, People’s Republic of China

    Ce Li, Bing Lu, Yachao Zhang & Hao Liu

  2. Xiamen University, Xiamen, Fujian Province, People’s Republic of China

    Yanyun Qu

Authors
  1. Ce Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bing Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yachao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hao Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yanyun Qu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yanyun Qu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, C., Lu, B., Zhang, Y. et al. 3D Reconstruction of Indoor Scenes via Image Registration. Neural Process Lett 48, 1281–1304 (2018). https://doi.org/10.1007/s11063-018-9781-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-018-9781-0

Keywords

Search

Navigation