Skip to main content

Top–Down Bayesian Inference of Indoor Scenes

  • Chapter
Advanced Topics in Computer Vision

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

  • 3043 Accesses

Abstract

The task of inferring the 3D layout of indoor scenes from images has seen many recent advancements. Understanding the basic 3D geometry of these environments is important for higher level applications, such as object recognition and robot navigation. In this chapter, we present our Bayesian generative model for understanding indoor environments. We model the 3D geometry of a room and the objects within it with non-overlapping 3D boxes, which provide approximations for both the room boundary and objects like tables and beds. We separately model the imaging process (camera parameters), and an image likelihood, thus providing a complete, generative statistical model for image data. A key feature of this work is using prior information and constraints on the 3D geometry of the scene elements, which addresses ambiguities in the imaging process in a top–down fashion. We also describe and relate this work to other state-of-the-art approaches, and discuss techniques that have become standard in this field, such as estimating the camera pose from a triplet of vanishing points.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover 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

Notes

  1. 1.

    http://www.ikea.com/us/en/catalog/categories/departments/bedroom/

  2. 2.

    http://www6.homedepot.com/cyber-monday/index.html

References

  1. Coughlan JM, Yuille AL (1999) Manhattan world: compass direction from a single image by Bayesian inference. In: ICCV

    Google Scholar 

  2. Del Pero L, Guan J, Brau E, Schlecht J, Barnard K (2011) Sampling bedrooms. In: CVPR

    Google Scholar 

  3. Del Pero L, Bowdish J, Fried D, Kermgard B, Hartley E, Barnard K (2012) Bayesian geometric modeling of indoor scenes. In: CVPR

    Google Scholar 

  4. Delage E, Lee HL, Ng AY (2005) Automatic single-image 3d reconstructions of indoor Manhattan world scenes. In: ISRR

    Google Scholar 

  5. Green PJ (1995) Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika 82(4):711–732

    Article  MathSciNet  MATH  Google Scholar 

  6. Green PJ (2003) Trans-dimensional Markov chain Monte Carlo. In: Highly structured stochastic systems

    Google Scholar 

  7. Gupta A, Satkin S, Efros AA, Hebert M (2011) From 3D scene geometry to human workspace. In: CVPR

    Google Scholar 

  8. Hartley RI, Zisserman A (2004) Multiple view geometry in computer vision. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

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

    Google Scholar 

  10. Hedau V, Hoiem D, Forsyth D (2010) Thinking inside the box: using appearance models and context based on room geometry. In: ECCV

    Google Scholar 

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

    Google Scholar 

  12. Hoiem D, Efros AA, Hebert M (2005) Geometric context from a single image. In: ICCV

    Google Scholar 

  13. Hoiem D, Efros AA, Hebert M (2006) Putting objects in perspective. In: CVPR

    Google Scholar 

  14. Karsch K, Hedau V, Forsyth D, Hoiem D (2011) Rendering synthetic objects into legacy photographs. In: SIGGRAPH Asia

    Google Scholar 

  15. Lee DC, Hebert M, Kanade T (2009) Geometric reasoning for single image structure recovery. In: CVPR

    Google Scholar 

  16. Lee DC, Gupta A, Hebert M, Kanade T (2010) Estimating spatial layout of rooms using volumetric reasoning about objects and surfaces. In: NIPS

    Google Scholar 

  17. Neal RM (1993) Probabilistic inference using Markov chain Monte Carlo methods. Technical report

    Google Scholar 

  18. Rother C (2002) A new approach to vanishing point detection in architectural. Image Vis Comput 20(9–10):647–655

    Article  Google Scholar 

  19. Schlecht J, Barnard K (2009) Learning models of object structure. In: NIPS

    Google Scholar 

  20. Schwing A, Hazan T, Pollefeys M, Urtasun R (2012) Efficient structure prediction with latent variables for general graphics models. In: CVPR

    Google Scholar 

  21. Shi F, Zhang X, Liu Y (2004) A new method of camera pose estimation using 2D-3D corner correspondence. Pattern Recognit Lett 25(10):1155–1163

    Article  Google Scholar 

  22. Tsai G, Xu C, Liu J, Kuipers B (2011) Real-time indoor scene understanding using Bayesian filtering with motion cues. In: ICCV

    Google Scholar 

  23. Tu Z, Zhu S (2002) Image segmentation by data-driven Markov chain Monte-Carlo. In: PAMI

    Google Scholar 

  24. Wang H, Gould S, Koller D (2010) Discriminative learning with latent variables for cluttered indoor scene understanding. In: ECCV

    Google Scholar 

  25. Yu SX, Zhang H, Malik J (2008) Inferring spatial layout from a single image via depth-ordered grouping, In: POCV

    Google Scholar 

  26. Zhu S-C, Zhang R, Tu Z (2000) Integrating top–down/bottom–up for object recognition by data driven Markov chain Monte Carlo. In: CVPR

    Google Scholar 

Download references

Acknowledgements

This material is based upon work supported by the National Science Foundation under Grant No. 0747511. We thank Joseph Schlecht for his contributions and suggestions in designing the code base. We also acknowledge the valuable help of Joshua Bowdish, Ernesto Brau, Andrew Emmott, Daniel Fried, Jinyan Guan, Emily Hartley, Bonnie Kermgard, and Philip Lee.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Luca Del Pero .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag London

About this chapter

Cite this chapter

Del Pero, L., Barnard, K. (2013). Top–Down Bayesian Inference of Indoor Scenes. In: Farinella, G., Battiato, S., Cipolla, R. (eds) Advanced Topics in Computer Vision. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-5520-1_10

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-5520-1_10

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-5519-5

  • Online ISBN: 978-1-4471-5520-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics