Unfolding an Indoor Origami World

In this work, we present a method for single-view reasoning about 3D surfaces and their relationships. We propose the use of mid-level constraints for 3D scene understanding in the form of convex and concave edges and introduce a generic framework capable of incorporating these and other constraints. Our method takes a variety of cues and uses them to infer a consistent interpretation of the scene. We demonstrate improvements over the state-of-the art and produce interpretations of the scene that link large planar surfaces.

[1]  Silvio Savarese,et al.  Estimating the aspect layout of object categories , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[2]  Sanja Fidler,et al.  Box in the Box: Joint 3D Layout and Object Reasoning from Single Images , 2013, 2013 IEEE International Conference on Computer Vision.

[3]  Tsuhan Chen,et al.  A learning-based framework for depth ordering , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Takeo Kanade,et al.  A Theory of Origami World , 1979, Artif. Intell..

[5]  Ashutosh Saxena,et al.  Learning Depth from Single Monocular Images , 2005, NIPS.

[6]  Tamir Hazan,et al.  Continuous Markov Random Fields for Robust Stereo Estimation , 2012, ECCV.

[7]  Xuming He,et al.  Discrete-Continuous Depth Estimation from a Single Image , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Song-Chun Zhu,et al.  Scene Parsing by Integrating Function, Geometry and Appearance Models , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Alexei A. Efros,et al.  Geometric context from a single image , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[10]  Tsuhan Chen,et al.  3D-Based Reasoning with Blocks, Support, and Stability , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Thomas Deselaers,et al.  ClassCut for Unsupervised Class Segmentation , 2010, ECCV.

[12]  Derek Hoiem,et al.  Indoor Segmentation and Support Inference from RGBD Images , 2012, ECCV.

[13]  Jaishanker K. Pillai,et al.  Manhattan Junction Catalogue for Spatial Reasoning of Indoor Scenes , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Alan L. Yuille,et al.  The Manhattan World Assumption: Regularities in Scene Statistics which Enable Bayesian Inference , 2000, NIPS.

[15]  Raquel Urtasun,et al.  Efficient Exact Inference for 3D Indoor Scene Understanding , 2012, ECCV.

[16]  Alexei A. Efros,et al.  Blocks World Revisited: Image Understanding Using Qualitative Geometry and Mechanics , 2010, ECCV.

[17]  Ce Liu,et al.  Depth Extraction from Video Using Non-parametric Sampling , 2012, ECCV.

[18]  Matthieu Guillaumin,et al.  Segmentation Propagation in ImageNet , 2012, ECCV.

[19]  Alexei A. Efros,et al.  Automatic photo pop-up , 2005, SIGGRAPH 2005.

[20]  Marc Pollefeys,et al.  Pulling Things out of Perspective , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[21]  D. A. Huffman,et al.  Impossible Objects as Nonsense Sentences , 2012 .

[22]  Takeo Kanade,et al.  Geometric reasoning for single image structure recovery , 2009, CVPR.

[23]  Jonathan T. Barron,et al.  Boundary Cues for 3D Object Shape Recovery , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[24]  Jitendra Malik,et al.  Perceptual Organization and Recognition of Indoor Scenes from RGB-D Images , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[25]  Derek Hoiem,et al.  Recovering the spatial layout of cluttered rooms , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[26]  K. Sugihara.  Machine interpretation of line drawings , 1986, MIT Press series in artificial intelligence.

[27]  Jianxiong Xiao,et al.  Localizing 3D cuboids in single-view images , 2012, NIPS.

[28]  Pushmeet Kohli,et al.  Exact inference in multi-label CRFs with higher order cliques , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[29]  Song-Chun Zhu,et al.  Image Parsing with Stochastic Scene Grammar , 2011, NIPS.

[30]  Derek Hoiem,et al.  Support Surface Prediction in Indoor Scenes , 2013, 2013 IEEE International Conference on Computer Vision.

[31]  Takeo Kanade,et al.  Estimating Spatial Layout of Rooms using Volumetric Reasoning about Objects and Surfaces , 2010, NIPS.

[32]  Jianxiong Xiao,et al.  A Linear Approach to Matching Cuboids in RGBD Images , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[33]  Daniel Fried,et al.  Bayesian geometric modeling of indoor scenes , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[34]  Song-Chun Zhu,et al.  Image Parsing via Stochastic Scene Grammar , 2011 .

[35]  Silvio Savarese,et al.  Understanding Indoor Scenes Using 3D Geometric Phrases , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[36]  Jian Zhang,et al.  Estimating the 3D Layout of Indoor Scenes and Its Clutter from Depth Sensors , 2013, 2013 IEEE International Conference on Computer Vision.

[37]  M. B. Clowes,et al.  On Seeing Things , 1971, Artif. Intell..

[38]  Martial Hebert,et al.  Data-Driven 3D Primitives for Single Image Understanding , 2013, 2013 IEEE International Conference on Computer Vision.

[39]  Jitendra Malik,et al.  Inferring spatial layout from a single image via depth-ordered grouping , 2008, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[40]  Alexei A. Efros,et al.  Recovering Occlusion Boundaries from an Image , 2011, International Journal of Computer Vision.

[41]  Honglak Lee,et al.  A Dynamic Bayesian Network Model for Autonomous 3D Reconstruction from a Single Indoor Image , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).