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.

中文翻译：

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Shape2Motion：3D 形状的运动部件和属性的联合分析

对于 3D 形状的移动性分析任务，我们建议对同时运动部分分割和运动属性估计进行联合分析，以单个 3D 模型作为输入。该问题与现有工作中解决的问题显着不同，现有工作假设存在预先存在的形状分割或不同运动状态下的多个 3D 模型。为此，我们开发了 Shape2Motion，它将单个 3D 点云作为输入，并联合计算面向移动性的分割和相关的运动属性。Shape2Motion 由两个深度神经网络组成，分别设计用于生成移动性建议和移动性优化。这些网络的主要贡献是用于运动部分分割和运动属性估计的新颖运动驱动特征和损失。这是基于观察到功能部件的运动保留了形状结构。我们使用新提出的用于 3D 形状移动性分析的基准来评估 Shape2Motion。结果表明，我们的方法在运动部分分割和运动属性估计方面都达到了最先进的性能。