Unsupervised Kinematic Motion Detection for Part-segmented 3D Shape Collections

• URL: http://arxiv.org/abs/2206.08497v1
• Date: Fri, 17 Jun 2022 00:50:36 GMT
• Title: Unsupervised Kinematic Motion Detection for Part-segmented 3D Shape Collections
• Authors: Xianghao Xu, Yifan Ruan, Srinath Sridhar, Daniel Ritchie
• Abstract summary: We present an unsupervised approach for discovering articulated motions in a part-segmented 3D shape collection. Our approach is based on a concept we call category closure: any valid articulation of an object's parts should keep the object in the same semantic category. We evaluate our approach by using it to re-discover part motions from the PartNet-Mobility dataset.
• Score: 14.899075941080541
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 3D models of manufactured objects are important for populating virtual worlds and for synthetic data generation for vision and robotics. To be most useful, such objects should be articulated: their parts should move when interacted with. While articulated object datasets exist, creating them is labor-intensive. Learning-based prediction of part motions can help, but all existing methods require annotated training data. In this paper, we present an unsupervised approach for discovering articulated motions in a part-segmented 3D shape collection. Our approach is based on a concept we call category closure: any valid articulation of an object's parts should keep the object in the same semantic category (e.g. a chair stays a chair). We operationalize this concept with an algorithm that optimizes a shape's part motion parameters such that it can transform into other shapes in the collection. We evaluate our approach by using it to re-discover part motions from the PartNet-Mobility dataset. For almost all shape categories, our method's predicted motion parameters have low error with respect to ground truth annotations, outperforming two supervised motion prediction methods.

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