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.

翻译：本文开发了用于4D全景分割的旋转等变神经网络。4D全景分割是自动驾驶的一项基准任务，要求基于激光雷达扫描识别道路上的语义类别和物体实例，并跨时间为实例分配时间一致性ID。我们观察到驾驶场景在地平面上具有旋转对称性。因此，旋转等变性能够提供更好的泛化能力和更鲁棒的特征学习。具体而言，我们回顾了物体实例聚类策略，并将基于中心度的方法和基于偏移量的方法重新表述为不变标量场和等变向量场的预测。其他子任务也从这个视角统一起来，并设计了不同的不变层和等变层以促进其预测。通过在SemanticKITTI标准4D全景分割基准上的评估，我们展示了等变模型相比非等变模型以更低的计算成本实现了更高的精度。此外，我们的方法达到了新的最先进性能，并在SemanticKITTI 4D全景分割排行榜上获得第一名。