A spatiotemporal correspondence approach to unsupervised LiDAR segmentation with traffic applications

Xiao Li, Pan He, Aotian Wu, Sanjay Ranka, Anand Rangarajan

Abstract:

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.