Abstract
In this paper we present a novel algorithm to estimate the surface layout of an indoor scene, which can serve as a visual cue for many different applications, e.g. 3D tracking, or localization in visual odometry. The main contribution of this work lies in combining multiple superpixel segmentation methods in order to obtain semantically meaningful regions. For each segmentation method, we combine 3D reasoning with semantic reasoning to generate multiple surface layout label hypotheses for each pixel. We then get the final label for each pixel within a Markov Random Field (MRF) by combining all hypothesis and by enforcing spatial consistency between neighboring pixels. Experimental results on complex indoor scenes show that our proposed method outperforms state-of-the-art methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Hoiem, D., Efros, A., Hebert, M.: Recovering surface layout from an image. IJCV 75(1) (2007)
Furukawa, Y., Ponce, J.: Accurate, dense, and robust multi-view stereopsis. In: CVPR (2007)
Saxena, A., Sun, M., Ng, A.: Make3d: Learning 3d scene structure from a single still image. PAMI 31(5), 824–840 (2009)
Lee, D., Hebert, M., Kanade, T.: Geometric reasoning for single image structure recovery. In: CVPR (2009)
Hedau, V., Hoiem, D., Forsyth, D.: Recovering the spatial layout of cluttered rooms. In: ICCV (2009)
Del Pero, L., Guan, J., Brau, E., Schlecht, J., Barnard, K.: Sampling bedrooms. In: CVPR, pp. 2009–2016 (2011)
Del Pero, L., Bowdish, J., Fried, D., Kermgard, B., Hartley, E., Barnard, K.: Bayesian geometric modeling of indoor scenes. In: CVPR (2012)
Flint, A., Murray, D., Reid, I.: Manhattan scene understanding using monocular, stereo, and 3d features. In: ICCV (2011)
Tsai, G., Xu, C., Liu, J., Kuipers, B.: Real-time indoor scene understanding using bayesian filtering with motion cues. In: ICCV, pp. 121–128 (2011)
Brostow, G.J., Shotton, J., Fauqueur, J., Cipolla, R.: Segmentation and recognition using structure from motion point clouds. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 44–57. Springer, Heidelberg (2008)
Snavely, N., Seitz, S., Szeliski, R.: Photo tourism: Exploring photo collections in 3d. ACM Transactions on Graphics, 835–846 (2006)
Felzenszwalb, P., Huttenlocher, D.: Efficient graph-based image segmentation. IJCV 59(2), 167–181 (2004)
Levinshtein, A., Stere, A., Kutulakos, K., Fleet, D., Dickinson, S., Siddiqi, K.: Turbopixels: Fast superpixels using geometric flows. PAMI 31(12), 2290–2297 (2009)
Liu, M.Y., Tuzel, O., Ramalingam, S., Chellappa, R.: Entropy rate superpixel segmentation. In: CVPR, pp. 2097–2104 (2011)
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Suesstrunk, S.: SLIC Superpixels Compared to State-of-the-art Superpixel Methods. PAMI (2012)
Felzenszwalb, P., Girshick, R., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. PAMI 32, 1627–1645 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hödlmoser, M., Micusik, B. (2013). Surface Layout Estimation Using Multiple Segmentation Methods and 3D Reasoning. In: Sanches, J.M., Micó, L., Cardoso, J.S. (eds) Pattern Recognition and Image Analysis. IbPRIA 2013. Lecture Notes in Computer Science, vol 7887. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38628-2_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-38628-2_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38627-5
Online ISBN: 978-3-642-38628-2
eBook Packages: Computer ScienceComputer Science (R0)