%0 Conference Paper %B Annual Conference on Neural Information Processing Systems (NIPS) %D 2017 %T Self-supervised intrinsic image decomposition. %A Michael Janner %A Jiajun Wu %A Tejas Kulkarni %A Ilker Yildirim %A Joshua B. Tenenbaum %B Annual Conference on Neural Information Processing Systems (NIPS) %C Long Beach, CA %8 12/2017 %G eng %U https://papers.nips.cc/paper/7175-self-supervised-intrinsic-image-decomposition %0 Conference Paper %B 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) %D 2017 %T Synthesizing 3D Shapes via Modeling Multi-view Depth Maps and Silhouettes with Deep Generative Networks %A Amir Arsalan Soltani %A Haibin Huang %A Jiajun Wu %A Tejas Kulkarni %A Joshua B. Tenenbaum %K 2d to 3d %K 3D generation %K 3D reconstruction %K Core object system %K depth map %K generative %K perception %K silhouette %X

We study the problem of learning generative models of 3D shapes. Voxels or 3D parts have been widely used as the underlying representations to build complex 3D shapes; however, voxel-based representations suffer from high memory requirements, and parts-based models require a large collection of cached or richly parametrized parts. We take an alternative approach: learning a generative model over multi-view depth maps or their corresponding silhouettes, and using a deterministic rendering function to produce 3D shapes from these images. A multi-view representation of shapes enables generation of 3D models with fine details, as 2D depth maps and silhouettes can be modeled at a much higher resolution than 3D voxels. Moreover, our approach naturally brings the ability to recover the underlying 3D representation from depth maps of one or a few viewpoints. Experiments show that our framework can generate 3D shapes with variations and details. We also demonstrate that our model has out-of-sample generalization power for real-world tasks with occluded objects.

%B 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) %C Honolulu, HI %8 07/2017 %G eng %U http://ieeexplore.ieee.org/document/8099752/http://xplorestaging.ieee.org/ielx7/8097368/8099483/08099752.pdf?arnumber=8099752 %R 10.1109/CVPR.2017.269 %0 Conference Paper %B Annual Conference of the Cognitive Science Society %D 2015 %T Efficient and robust analysis-by-synthesis in vision: A computational framework, behavioral tests, and modeling neuronal representations %A Ilker Yildirim %A Tejas Kulkarni %A W. A. Freiwald %A Joshua B. Tenenbaum %B Annual Conference of the Cognitive Science Society %G eng %0 Conference Paper %B Computer Vision and Pattern Recognition %D 2015 %T Picture: An Imperative Probabilistic Programming Language for Scene Perception %A Tejas Kulkarni %A Pushmeet Kohli %A Joshua B. Tenenbaum %A Vikash Mansinghka %B Computer Vision and Pattern Recognition %G eng %0 Generic %D 2014 %T Explaining Monkey Face Patch System as Efficient Analysis-by-Synthesis %A Ilker Yildirim %A Tejas Kulkarni %A W. A. Freiwald %A Joshua B. Tenenbaum