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.

我们解决了在各种交通场景下，对室外 LiDAR 点云进行无监督语义分割的问题，通过建立多帧之间的时空对应关系，将点云序列的时空特征引入强化扩充，融合聚类和伪标签学习，以无监督的学习方式学习辨别特征，展现出在自动驾驶车辆和交叉口基础设施方面的分割性能在 Semantic-KITTI，SemanticPOSS 和 FLORIDA 基准数据集上表现得很有竞争力，该通用框架可以为 LiDAR 点云的统一表示学习方法融入领域知识。