Skip to main content

3D Aware Correction and Completion of Depth Maps in Piecewise Planar Scenes

  • Conference paper
  • First Online:
Computer Vision -- ACCV 2014 (ACCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9004))

Included in the following conference series:

Abstract

RGB-D sensors are popular in the computer vision community, especially for problems of scene understanding, semantic scene labeling, and segmentation. However, most of these methods depend on reliable input depth measurements, while discarding unreliable ones. This paper studies how reliable depth values can be used to correct the unreliable ones, and how to complete (or extend) the available depth data beyond the raw measurements of the sensor (i.e. infer depth at pixels with unknown depth values), given a prior model on the 3D scene. We consider piecewise planar environments in this paper, since many indoor scenes with man-made objects can be modeled as such. We propose a framework that uses the RGB-D sensor’s noise profile to adaptively and robustly fit plane segments (e.g. floor and ceiling) and iteratively complete the depth map, when possible. Depth completion is formulated as a discrete labeling problem (MRF) with hard constraints and solved efficiently using graph cuts. To regularize this problem, we exploit 3D and appearance cues that encourage pixels to take on depth values that will be compatible in 3D to the piecewise planar assumption. Extensive experiments, on a new large-scale and challenging dataset, show that our approach results in more accurate depth maps (with 20 % more depth values) than those recorded by the RGB-D sensor. Additional experiments on the NYUv2 dataset show that our method generates more 3D aware depth. These generated depth maps can also be used to improve the performance of a state-of-the-art RGB-D SLAM method.

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Barron, J.T., Malik, J., Berkeley, U.C.: Intrinsic scene properties from a single RGB-D image. In: CVPR (2013)

    Google Scholar 

  2. Bazin, J., Seo, Y.: Globally optimal line clustering and vanishing point estimation in manhattan world. In: CVPR (2012)

    Google Scholar 

  3. Boykov, Y., Funka-Lea, G.: Graph cuts and efficient N-D image segmentation. IJCV 70(2), 109–131 (2006)

    Article  Google Scholar 

  4. Camplani, M., Salgado, L.: Efficient spatio-temporal hole filling strategy for kinect depth maps. In: SPIE (2012)

    Google Scholar 

  5. Cheung, S.C.S.: Layer depth denoising and completion for structured-light RGB-D cameras. In: CVPR (2013)

    Google Scholar 

  6. Diebel, J., Thrun, S.: An application of markov random fields to range sensing. In: NIPS (2005)

    Google Scholar 

  7. Endres, F., Hess, J., Engelhard, N., Sturm, J., Cremers, D., Burgard, W.: An evaluation of the RGB-D SLAM system. In: ICRA (2012)

    Google Scholar 

  8. Flint, A., Murray, D., Reid, I.: Manhattan scene understanding using monocular, stereo, and 3D features. In: ICCV (2011)

    Google Scholar 

  9. Furukawa, Y., Curless, B., Seitz, S., Szeliski, R.: Manhattan-world stereo. In: CVPR (2009)

    Google Scholar 

  10. Furukawa, Y., Curless, B., Seitz, S.M., Szeliski, R.: Reconstructing building interiors from images. In: ICCV (2009)

    Google Scholar 

  11. Gallup, D., Frahm, J.M., Pollefeys, M.: Piecewise planar and non-planar stereo for urban scene reconstruction. In: CVPR (2012)

    Google Scholar 

  12. Ghanem, B., Ahuja, N.: Dinkelbach NCUT: An efficient framework for solving normalized cuts problems with priors and convex constraints. IJCV 89(1), 40–55 (2010)

    Article  Google Scholar 

  13. Gupta, S., Arbel, P., Malik, J., Berkeley, B.: Perceptual organization and recognition of indoor scenes from RGB-D images. In: CVPR (2013)

    Google Scholar 

  14. Hedau, V., Hoiem, D., Forsyth, D.: Recovering the spatial layout of cluttered rooms. In: CVPR (2009)

    Google Scholar 

  15. Hedau, V., Hoiem, D., Forsyth, D.: Thinking inside the box: using appearance models and context based on room geometry. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part VI. LNCS, vol. 6316, pp. 224–237. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  16. Hedau, V., Hoiem, D., Forsyth, D.: Recovering free space of indoor scenes from a single image. In: CVPR (2012)

    Google Scholar 

  17. Henry, P., Krainin, M., Herbst, E., Ren, X., Fox, D.: RGB-D mapping: using kinect-style depth cameras for dense 3D modeling of indoor environments. IJRR 31(5), 647–663 (2012)

    Google Scholar 

  18. Hu, G., Huang, S., Zhao, L.: A robust RGB-D SLAM algorithm. In: IROS (2012)

    Google Scholar 

  19. Jia, Z., Gallagher, A., Saxena, A., Chen, T.: 3D-based reasoning with blocks, support, and stability. In: CVPR (2013)

    Google Scholar 

  20. Kim, B.s., Arbor, A., Savarese, S.: Accurate localization of 3D objects from RGB-D data using segmentation hypotheses. In: CVPR (2013)

    Google Scholar 

  21. Kopf, J., Cohen, M.: Joint bilateral upsampling. In: SIGGRAPH (2007)

    Google Scholar 

  22. Koppula, H.S., Anand, A., Joachims, T., Saxena, A.: Semantic labeling of 3D point clouds for indoor scenes. In: NIPS (2011)

    Google Scholar 

  23. Levin, A., Lischinski, D., Weiss, Y.: Colorization using optimization. In: SIGGRAPH (2004)

    Google Scholar 

  24. Park, J., Kim, H., Brown, M.S., Kweon, I.: High quality depth map upsampling for 3D-TOF cameras. In: ICCV (2011)

    Google Scholar 

  25. Silberman, N., Hoiem, D., Kohli, P., Fergus, R.: Indoor segmentation and support inference from RGBD images. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part V. LNCS, vol. 7576, pp. 746–760. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  26. Sturm, J., Engelhard, N., Endres, F., Burgard, W., Cremers, D.: A benchmark for the evaluation of RGB-D SLAM systems. In: IROS (2012)

    Google Scholar 

  27. Wang, L., Jin, H., Yang, R., Gong, M.: Stereoscopic inpainting: joint color and depth completion from stereo images. In: CVPR (2008)

    Google Scholar 

Download references

Acknowledgement

Research reported in this publication was supported by competitive research funding from King Abdullah University of Science and Technology (KAUST).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Bernard Ghanem .

Editor information

Editors and Affiliations

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material (zip 18,404 KB)

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Thabet, A.K., Lahoud, J., Asmar, D., Ghanem, B. (2015). 3D Aware Correction and Completion of Depth Maps in Piecewise Planar Scenes. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9004. Springer, Cham. https://doi.org/10.1007/978-3-319-16808-1_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-16808-1_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16807-4

  • Online ISBN: 978-3-319-16808-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics