Prateek Singhal
ABSTRACT
Detecting multiple planes in images is a challenging problem, but one with many applications. Recent work such as J-Linkage and Ordered Residual Kernels have focussed on developing a domain independent approach to detect multiple structures. These multiple structure detection methods are then used for estimating multiple homographies given feature matches between two images. Features participating in the multiple homographies detected, provide us the multiple scene planes. We show that these methods provide locally optimal results and fail to merge detected planar patches to the true scene planes. These methods use only residues obtained on applying homography of one plane to another as cue for merging. In this paper, we develop additional cues such as local consistency of planes, local normals, texture etc. to perform better classification and merging. We formulate the classification as an MRF problem and use TRWS message passing algorithm to solve non metric energy terms and complex sparse graph structure. We show results on Michigan Indoor Corridor Dataset and our challenging dataset, common in robotics navigation scenarios. Experiments on the datasets demonstrate the accuracy of our plane detection relative to ground truth, with detailed comparisons to prior art.
- A. Agarwal, C. V. Jawahar, and P. J. Narayanan. A survey of planar homography estimation techniques. Technical report, 2005.Google Scholar
- T.-J. Chin, H. Wang, and D. Suter. Robust fitting of multiple structures: The statistical learning approach. In ICCV, pages 413–420, 2009.Google Scholar
- D. F. Fouhey, D. Scharstein, and A. J. Briggs. Multiple plane detection in image pairs using j-linkage. In Int. Conf. on Pattern Recognition, 2010. Google ScholarDigital Library
- V. Hedau, D. Hoiem, and D. A. Forsyth. Recovering the spatial layout of cluttered rooms. In ICCV, pages 1849–1856, 2009.Google ScholarCross Ref
- S. Jain and V. M. Govindu. Efficient higher order clustering on the grassmann manifold. In ICCV, 2013. Google ScholarDigital Library
- O. Kähler and J. Denzler. Detecting coplanar feature points in handheld image sequences. In In Proceedings Conference on Computer Vision Theory and Applications, VISAPP 2007, pages 447–452. INSTICC Press, 2007.Google Scholar
- O. Kähler and J. Denzler. Tracking and reconstruction in a combined optimization approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(2):387–401, 2012. Google ScholarDigital Library
- D. S. Kumar and C. Jawahar. Robust homography-based control for camera positioning in piecewise planar environments. In Computer Vision, Graphics and Image Processing, pages 906–918. Springer Berlin Heidelberg, 2006. Google ScholarDigital Library
- T.-T. Pham, T.-J. Chin, J. Yu, and D. Suter. The random cluster model for robust geometric fitting. In CVPR, pages 710–717. IEEE, 2012. Google ScholarDigital Library
- R. Szeliski, R. Zabih, D. Scharstein, O. Veksler, A. Agarwala, and C. Rother. A comparative study of energy minimization methods for markov random fields. In In ECCV, pages 16–29, 2006. Google ScholarDigital Library
- R. Toldo and A. Fusiello. Robust multiple structures estimation with j-linkage. In Proceedings of the 10th European Conference on Computer Vision: Part I, ECCV '08, pages 537–547, Berlin, Heidelberg, 2008. Springer-Verlag. Google ScholarDigital Library
- Z. Zhou, H. Jin, and Y. Ma. Robust plane-based structure from motion. In CVPR, pages 1482–1489, 2012. Google ScholarDigital Library
- Z. Zhou, H. Jin, and Y. Ma. Plane-based content preserving warps for video stabilization. In CVPR, pages 2299–2306, 2013. Google ScholarDigital Library
- M. Zuliani, C. Kenney, and B. Manjunath. The multiransac algorithm and its application to detect planar homographies. In ICIP 2005., volume 3, pages III–153–6, 2005.Google ScholarCross Ref
- D. Lin, S. Fidler, and R. Urtasun. Holistic Scene Understanding for 3D Object Detection with RGBD Cameras, In ICCV, pages 1414–1424, 2013. Google ScholarDigital Library
- G. Tsai, C. Xu, J. Liu, and B. Kuipers Real-time indoor scene understanding using Bayesian filtering with motion cues In ICCV, pages 121–128, 2011. Google ScholarDigital Library
- www.http://www.cc.gatech.edu/psinghal/ICVGIP2014Google Scholar
Index Terms
- Top Down Approach to Detect Multiple Planes from Pair of Images
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