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。