Temporal semantic scene understanding is critical for self-driving cars or robots operating in dynamic environments. In this paper, we propose 4D panoptic LiDAR segmentation to assign a semantic class and a temporally-consistent instance ID to a sequence of 3D points. To this end, we present an approach and a point-centric evaluation metric. Our approach determines a semantic class for every point while modeling object instances as probability distributions in the 4D spatio-temporal domain. We process multiple point clouds in parallel and resolve point-to-instance associations, effectively alleviating the need for explicit temporal data association. Inspired by recent advances in benchmarking of multi-object tracking, we propose to adopt a new evaluation metric that separates the semantic and point-to-instance association aspects of the task. With this work, we aim at paving the road for future developments of temporal LiDAR panoptic perception.

提出一种新的4D全景LiDAR分割方法，将语义类和时间一致的实例ID分配给3D点序列，使用点为中心的评估度量来确定每个点的语义类别，将对象实例建模为4D时空域中的概率分布，从而以更高效的方式处理多个点云。这项工作展望未来的超高效LiDAR全景感知。

4D全景式激光雷达分割