Discovering 3D Parts from Image Collections

• URL: http://arxiv.org/abs/2107.13629v1
• Date: Wed, 28 Jul 2021 20:29:16 GMT
• Title: Discovering 3D Parts from Image Collections
• Authors: Chun-Han Yao, Wei-Chih Hung, Varun Jampani, Ming-Hsuan Yang
• Abstract summary: We tackle the problem of 3D part discovery from only 2D image collections. Instead of relying on manually annotated parts for supervision, we propose a self-supervised approach. Our key insight is to learn a novel part shape prior that allows each part to fit an object shape faithfully while constrained to have simple geometry.
• Score: 98.16987919686709
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reasoning 3D shapes from 2D images is an essential yet challenging task, especially when only single-view images are at our disposal. While an object can have a complicated shape, individual parts are usually close to geometric primitives and thus are easier to model. Furthermore, parts provide a mid-level representation that is robust to appearance variations across objects in a particular category. In this work, we tackle the problem of 3D part discovery from only 2D image collections. Instead of relying on manually annotated parts for supervision, we propose a self-supervised approach, latent part discovery (LPD). Our key insight is to learn a novel part shape prior that allows each part to fit an object shape faithfully while constrained to have simple geometry. Extensive experiments on the synthetic ShapeNet, PartNet, and real-world Pascal 3D+ datasets show that our method discovers consistent object parts and achieves favorable reconstruction accuracy compared to the existing methods with the same level of supervision.

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