@conference {3596,
	title = {Self-supervised intrinsic image decomposition.},
	booktitle = { Annual Conference on Neural Information Processing Systems (NIPS)},
	year = {2017},
	month = {12/2017},
	address = {Long Beach, CA},
	url = {https://papers.nips.cc/paper/7175-self-supervised-intrinsic-image-decomposition},
	author = {Michael Janner and Jiajun Wu and Tejas Kulkarni and Ilker Yildirim and Joshua B. Tenenbaum}
}
@conference {4235,
	title = {Synthesizing 3D Shapes via Modeling Multi-view Depth Maps and Silhouettes with Deep Generative Networks},
	booktitle = {2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
	year = {2017},
	month = {07/2017},
	address = {Honolulu, HI},
	abstract = {<p>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.</p>
},
	keywords = {2d to 3d, 3D generation, 3D reconstruction, Core object system, depth map, generative, perception, silhouette},
	doi = {10.1109/CVPR.2017.269},
	url = {http://ieeexplore.ieee.org/document/8099752/http://xplorestaging.ieee.org/ielx7/8097368/8099483/08099752.pdf?arnumber=8099752},
	author = {Amir Arsalan Soltani and Haibin Huang and Jiajun Wu and Tejas Kulkarni and Joshua B. Tenenbaum}
}
@conference {1045,
	title = {Efficient and robust analysis-by-synthesis in vision: A computational framework, behavioral tests, and modeling neuronal representations},
	booktitle = {Annual Conference of the Cognitive Science Society},
	year = {2015},
	author = {Ilker Yildirim and Tejas Kulkarni and W. A. Freiwald and Joshua B. Tenenbaum}
}
@conference {1084,
	title = {Picture: An Imperative Probabilistic Programming Language for Scene Perception},
	booktitle = {Computer Vision and Pattern Recognition},
	year = {2015},
	author = {Tejas Kulkarni and Pushmeet Kohli and Joshua B. Tenenbaum and Vikash Mansinghka}
}
@article {1037,
	title = {Explaining Monkey Face Patch System as Efficient Analysis-by-Synthesis},
	year = {2014},
	author = {Ilker Yildirim and Tejas Kulkarni and W. A. Freiwald and Joshua B. Tenenbaum}
}
