iccv iccv2013 iccv2013-410 iccv2013-410-reference knowledge-graph by maker-knowledge-mining
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Author: Ruiqi Guo, Derek Hoiem
Abstract: In this paper, we present an approach to predict the extent and height of supporting surfaces such as tables, chairs, and cabinet tops from a single RGBD image. We define support surfaces to be horizontal, planar surfaces that can physically support objects and humans. Given a RGBD image, our goal is to localize the height and full extent of such surfaces in 3D space. To achieve this, we created a labeling tool and annotated 1449 images with rich, complete 3D scene models in NYU dataset. We extract ground truth from the annotated dataset and developed a pipeline for predicting floor space, walls, the height and full extent of support surfaces. Finally we match the predicted extent with annotated scenes in training scenes and transfer the the support surface configuration from training scenes. We evaluate the proposed approach in our dataset and demonstrate its effectiveness in understanding scenes in 3D space.
[1] M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The PASCAL Visual Object Classes Challenge 2010 Results. http://www.pascalnetwork.org/challenges/VOC/voc2010/workshop/index.html. 7
[2] Y. Furukawa, B. Curless, S. M. Seitz, and R. Szeliski. Reconstructing building interiors from images. In ICCV, pages 80–87, 2009. 2 2150 ?????????????? ?????????????? ???????????????? ???????????? ?????????????? ?????????????? ???????????????? ???????????? Figure 6: Overhead visualization. Green and blue and red areas are estimated walls, floor and support surfaces respectively. The brighter colors of support surfaces indicate higher vertical heights relative to the floor. Dark areas are out of the view scope.
[3] R. Guo and D. Hoiem. Beyond the line of sight: Labeling the underlying surfaces. In ECCV, pages 761–774, 2012. 1, 2
[4] A. Gupta, A. A. Efros, and M. Hebert. Blocks world re-
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15] visited: Image understanding using qualitative geometry and mechanics. In ECCV, 2010. 2 A. Gupta, S. Satkin, A. A. Efros, and M. Hebert. From 3d scene geometry to human workspace. In CVPR, 2011. 2 V. Hedau, D. Hoiem, and D. Forsyth. Recovering the spatial layout of cluttered rooms. In ICCV, 2009. 2 V. Hedau, D. Hoiem, and D. Forsyth. Thinking inside the box: Using appearance models and context based on room geometry. In ECCV, 2010. 2 V. Hedau, D. Hoiem, and D. A. Forsyth. Recovering free space of indoor scenes from a single image. In CVPR, pages 2807–2814, 2012. 2 Y. Jiang, M. Lim, C. Zheng, and A. Saxena. Learning to place new objects in a scene. I. J. Robotic Res., 3 1(9): 1021– 1043, 2012. 2 D. C. Lee, M. Hebert, and T. Kanade. Geometric reasoning for single image structure recovery. In CVPR, 2009. 2 S. Satkin, J. Lin, and M. Hebert. Data-driven scene understanding from 3D models. In BMVC, 2012. 2, 6 A. G. Schwing, T. Hazan, M. Pollefeys, and R. Urtasun. Efficient structured prediction for 3d indoor scene understanding. In CVPR, pages 2815–2822, 2012. 2 T. Shao, W. Xu, K. Zhou, J. Wang, D. Li, and B. Guo. An interactive approach to semantic modeling of indoor scenes with an rgbd camera. ACM Trans. Graph., 3 1(6): 136, 2012. 2 N. Silberman, D. Hoiem, P. Kohli, and R. Fergus. Indoor segmentation and support inference from rgbd images. In ECCV, pages 746–760, 2012. 1, 2, 3, 4, 7 C. J. Taylor and A. Cowley. Parsing indoor scenes using rgbd imagery. In Robotics: Science and Systems, 2012. 2
[16] Z. Tu and X. Bai. Auto-context and its application to highlevel vision tasks and 3d brain image segmentation. IEEE Trans. Pattern Anal. Mach. Intell., 32(10): 1744–1757, 2010. 2
[17] J. Xiao and Y. Furukawa. Reconstructing the world’s museums. In ECCV (1), pages 668–681, 2012. 2 215 1