Shape2Motion: Joint Analysis of Motion Parts and Attributes from 3D Shapes
This addresses the challenge of analyzing mobility in 3D shapes without requiring pre-existing segmentations or multiple models, which is incremental but useful for applications in robotics or computer graphics.
The paper tackles the problem of mobility analysis of 3D shapes by proposing Shape2Motion, which jointly segments motion parts and estimates motion attributes from a single 3D point cloud, achieving state-of-the-art performance on a new benchmark.
For the task of mobility analysis of 3D shapes, we propose joint analysis for simultaneous motion part segmentation and motion attribute estimation, taking a single 3D model as input. The problem is significantly different from those tackled in the existing works which assume the availability of either a pre-existing shape segmentation or multiple 3D models in different motion states. To that end, we develop Shape2Motion which takes a single 3D point cloud as input, and jointly computes a mobility-oriented segmentation and the associated motion attributes. Shape2Motion is comprised of two deep neural networks designed for mobility proposal generation and mobility optimization, respectively. The key contribution of these networks is the novel motion-driven features and losses used in both motion part segmentation and motion attribute estimation. This is based on the observation that the movement of a functional part preserves the shape structure. We evaluate Shape2Motion with a newly proposed benchmark for mobility analysis of 3D shapes. Results demonstrate that our method achieves the state-of-the-art performance both in terms of motion part segmentation and motion attribute estimation.