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Point Spatio-Temporal Transformer Networks for Point Cloud Video Modeling

Introduction

Due to the inherent unorderliness and irregularity of point cloud, points emerge inconsistently across different frames in a point cloud video. To capture the dynamics in point cloud videos, tracking points and limiting temporal modeling range are usually employed to preserve spatio-temporal structure. However, as points may flow in and out across frames, computing accurate point trajectories is extremely difficult, especially for long videos. Moreover, when points move fast, even in a small temporal window, points may still escape from a region. Besides, using the same temporal range for different motions may not accurately capture the temporal structure. In this paper, we propose a Point Spatio-Temporal Transformer (PST-Transformer). To preserve the spatio-temporal structure, PST-Transformer adaptively searches related or similar points across the entire video by performing self-attention on point features. Moreover, our PST-Transformer is equipped with an ability to encode spatio-temporal structure. Because point coordinates are irregular and unordered but point timestamps exhibit regularities and order, the spatio-temporal encoding is decoupled to reduce the impact of the spatial irregularity on the temporal modeling. By properly preserving and encoding spatio-temporal structure, our PST-Transformer effectively models point cloud videos and shows superior performance on 3D action recognition and 4D semantic segmentation.

Installation

The code is tested with Red Hat Enterprise Linux Workstation release 7.7 (Maipo), g++ (GCC) 9.4.0, PyTorch (v1.10.2), CUDA 11.3.1 and cuDNN v8.2.0.

Compile the CUDA layers for PointNet++, which we used for furthest point sampling (FPS) and radius neighbouring search:

cd modules
python setup.py install