2/4/2025 | 3:30 PM - 5:30 PM | Grand Peninsula A

LiDAR panoptic segmentation for autonomous driving: A survey

Author(s)

Aditya Dusi | Stanford University
Bassam Helou | Motional AD Inc.

Abstract

This survey provides a comprehensive overview of LiDAR-

based panoptic segmentation methods for autonomous driving.

We motivate the importance of panoptic segmentation in

autonomous vehicle perception, emphasizing its advantages over

traditional 3D object detection in capturing a more detailed and

comprehensive understanding of the environment. We summarize

and categorize 42 panoptic segmentation methods based on

their architectural approaches, with a focus on the kind of

clustering utilized- machine learned or non-learned heuristic

clustering. We discuss direct methods, most of which use single-

stage architectures to predict binary masks for each instance,

and clustering-based methods, most of which predict offsets to

object centers for efficient clustering. We also highlight relevant

datasets, evaluation metrics, and compile performance results on

SemanticKITTI and panoptic nuScenes benchmarks. Our analysis

reveals trends in the field, including the effectiveness of attention

mechanisms, the competitiveness of center-based approaches,

and the benefits of multi-modal sensor fusion. This survey aims

to guide practitioners in selecting suitable architectures and to

inspire researchers in identifying promising directions for future

work in LiDAR-based panoptic segmentation for autonomous

driving.