A Spatiotemporal Correspondence Approach to Unsupervised LiDAR Segmentation with Traffic Applications

• URL: http://arxiv.org/abs/2308.12433v1
• Date: Wed, 23 Aug 2023 21:32:46 GMT
• Title: A Spatiotemporal Correspondence Approach to Unsupervised LiDAR Segmentation with Traffic Applications
• Authors: Xiao Li, Pan He, Aotian Wu, Sanjay Ranka, Anand Rangarajan
• Abstract summary: Key idea is to leverage the nature of a dynamic point cloud sequence and introduce drastically stronger scenarios. We alternate between optimizing semantic into groups and clustering using point-wisetemporal labels. Our method can learn discriminative features in an unsupervised learning fashion.
• Score: 16.260518238832887
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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