Synthesizing 3D Shapes via Modeling Multi-view Depth Maps and Silhouettes with Deep Generative Networks

TitleSynthesizing 3D Shapes via Modeling Multi-view Depth Maps and Silhouettes with Deep Generative Networks
Publication TypeConference Paper
Year of Publication2017
AuthorsSoltani, AArsalan, Huang, H, Wu, J, Kulkarni, T, Tenenbaum, JB
Conference Name2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Date Published07/2017
Conference LocationHonolulu, HI
Keywords2d to 3d, 3D generation, 3D reconstruction, Core object system, depth map, generative, perception, silhouette
Abstract

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.

URLhttp://ieeexplore.ieee.org/document/8099752/http://xplorestaging.ieee.org/ielx7/8097368/8099483/08099752.pdf?arnumber=8099752
DOI10.1109/CVPR.2017.269
Refereed DesignationRefereed

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