A Spatiotemporal Correspondence Approach to Unsupervised LiDAR
  Segmentation with Traffic Applications
Xiao Li Pan He Aotian Wu Sanjay Ranka Anand Rangarajan
We address the problem of unsupervised semantic segmentation of outdoor LiDAR
point clouds in diverse traffic scenarios. The key idea is to leverage the
spatiotemporal nature of a dynamic point cloud sequence and introduce
drastically stronger augmentation by establishing spatiotemporal
correspondences across multiple frames. We dovetail clustering and pseudo-label
learning in this work. Essentially, we alternate between clustering points into
semantic groups and optimizing models using point-wise pseudo-spatiotemporal
labels with a simple learning objective. Therefore, our method can learn
discriminative features in an unsupervised learning fashion. We show promising
segmentation performance on Semantic-KITTI, SemanticPOSS, and FLORIDA benchmark
datasets covering scenarios in autonomous vehicle and intersection
infrastructure, which is competitive when compared against many existing fully
supervised learning methods. This general framework can lead to a unified
representation learning approach for LiDAR point clouds incorporating domain
knowledge.