CVPR 2023 - Spatiotemporal Self-supervised Learning for Point Clouds in the Wild

In this episode we discuss Spatiotemporal Self-supervised Learning for Point Clouds in the Wild by Yanhao Wu, Tong Zhang, Wei Ke, Sabine Süsstrunk, Mathieu Salzmann. The paper discusses a new self-supervised learning strategy for semantic segmentation of point clouds that leverages positive pairs in both the spatial and temporal domain. The authors designed a point-to-cluster learning strategy to distinguish objects and a cluster-to-cluster learning strategy based on unsupervised object tracking that exploits temporal correspondences. The approach was demonstrated through extensive experiments showing improved performance over state-of-the-art point cloud SSL methods on two large-scale LiDAR datasets and transferring models to other point cloud segmentation benchmarks.