  In this paper, we develop rotation-equivariant neural networks for 4D
panoptic segmentation. 4D panoptic segmentation is a benchmark task for
autonomous driving that requires recognizing semantic classes and object
instances on the road based on LiDAR scans, as well as assigning temporally
consistent IDs to instances across time. We observe that the driving scenario
is symmetric to rotations on the ground plane. Therefore, rotation-equivariance
could provide better generalization and more robust feature learning.
Specifically, we review the object instance clustering strategies and restate
the centerness-based approach and the offset-based approach as the prediction
of invariant scalar fields and equivariant vector fields. Other sub-tasks are
also unified from this perspective, and different invariant and equivariant
layers are designed to facilitate their predictions. Through evaluation on the
standard 4D panoptic segmentation benchmark of SemanticKITTI, we show that our
equivariant models achieve higher accuracy with lower computational costs
compared to their non-equivariant counterparts. Moreover, our method sets the
new state-of-the-art performance and achieves 1st place on the SemanticKITTI 4D
Panoptic Segmentation leaderboard.
