Part mobility analysis is a significant aspect required to achieve a functional understanding of 3D objects. It would be natural to obtain part mobility from the continuous part motion of 3D objects. In this study, we introduce a self-supervised method for segmenting motion parts and predicting their motion attributes from a point cloud sequence representing a dynamic object. To sufficiently utilize spatiotemporal information from the point cloud sequence, we generate trajectories by using correlations among successive frames of the sequence instead of directly processing the point clouds. We propose a novel neural network architecture called PointRNN to learn feature representations of trajectories along with their part rigid motions. We evaluate our method on various tasks including motion part segmentation, motion axis prediction and motion range estimation. The results demon strate that our method outperforms previous techniques on both synthetic and real datasets. Moreover, our method has the ability to generalize to new and unseen objects. It is important to emphasize that it is not required to know any prior shape structure, prior shape category information or shape orientation. To the best of our knowledge, this is the first study on deep learning to extract part mobility from point cloud sequence of a dynamic object.

中文翻译：

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基于点云序列的零件移动性自监督学习

零件移动性分析是实现对 3D 对象的功能理解所需的一个重要方面。从 3D 对象的连续部分运动中获得部分移动性是很自然的。在这项研究中，我们引入了一种自监督方法，用于分割运动部件并从表示动态对象的点云序列中预测它们的运动属性。为了充分利用来自点云序列的时空信息，我们通过使用序列的连续帧之间的相关性而不是直接处理点云来生成轨迹。我们提出了一种称为 PointRNN 的新型神经网络架构来学习轨迹的特征表示及其部分刚性运动。我们在各种任务上评估我们的方法，包括运动部分分割，运动轴预测和运动范围估计。结果表明，我们的方法在合成数据集和真实数据集上都优于以前的技术。此外，我们的方法能够推广到新的和看不见的对象。需要强调的是，不需要知道任何先验形状结构、先验形状类别信息或形状方向。据我们所知，这是第一个从动态对象的点云序列中提取部分移动性的深度学习研究。先验形状类别信息或形状方向。据我们所知，这是第一个从动态对象的点云序列中提取部分移动性的深度学习研究。先验形状类别信息或形状方向。据我们所知，这是第一个从动态对象的点云序列中提取部分移动性的深度学习研究。