论文标题
PSTNET：点云序列上的点时空卷积
PSTNet: Point Spatio-Temporal Convolution on Point Cloud Sequences
论文作者
论文摘要
点云序列在空间维度中是不规则的，并且在时间维度中表现出规律和顺序时是无序的。因此，现有的基于网格的常规视频处理的卷积不能直接应用于原始点云序列的时空建模。在本文中，我们提出了点时空（PST）卷积，以实现点云序列的信息表示。所提出的PST卷积首先将空间和时间放在点云序列中。然后，使用空间卷积来捕获3D空间中点的局部结构，并使用时间卷积来对空间区域的动力学进行建模。此外，我们将提出的PST卷积纳入一个深网，即PSTNET，以层次的方式提取点云序列的特征。对广泛使用的3D动作识别和4D语义分割数据集进行了广泛的实验，证明了PSTNET对模型点云序列的有效性。
Point cloud sequences are irregular and unordered in the spatial dimension while exhibiting regularities and order in the temporal dimension. Therefore, existing grid based convolutions for conventional video processing cannot be directly applied to spatio-temporal modeling of raw point cloud sequences. In this paper, we propose a point spatio-temporal (PST) convolution to achieve informative representations of point cloud sequences. The proposed PST convolution first disentangles space and time in point cloud sequences. Then, a spatial convolution is employed to capture the local structure of points in the 3D space, and a temporal convolution is used to model the dynamics of the spatial regions along the time dimension. Furthermore, we incorporate the proposed PST convolution into a deep network, namely PSTNet, to extract features of point cloud sequences in a hierarchical manner. Extensive experiments on widely-used 3D action recognition and 4D semantic segmentation datasets demonstrate the effectiveness of PSTNet to model point cloud sequences.
