RangeSeg: Range-Aware Real Time Segmentation of 3D LiDAR Point Clouds

TL;DR

RangeSeg introduces a range-aware segmentation network for 3D LiDAR point clouds, enhancing small and distant object detection while achieving real-time performance on embedded systems.

Contribution

The paper proposes a novel range-aware network architecture with dual decoders for improved segmentation accuracy and efficiency in 3D LiDAR data processing.

Findings

Outperforms state-of-the-art methods on KITTI dataset

Achieves real-time processing at 19 FPS on NVIDIA Jetson AGX Xavier

Significantly improves detection of small and far objects

Abstract

Semantic outdoor scene understanding based on 3D LiDAR point clouds is a challenging task for autonomous driving due to the sparse and irregular data structure. This paper takes advantages of the uneven range distribution of different LiDAR laser beams to propose a range aware instance segmentation network, RangeSeg. RangeSeg uses a shared encoder backbone with two range dependent decoders. A heavy decoder only computes top of a range image where the far and small objects locate to improve small object detection accuracy, and a light decoder computes whole range image for low computational cost. The results are further clustered by the DBSCAN method with a resolution weighted distance function to get instance-level segmentation results. Experiments on the KITTI dataset show that RangeSeg outperforms the state-of-the-art semantic segmentation methods with enormous speedup and improves the instance-level segmentation performance on small and far objects. The whole RangeSeg pipeline meets the real time requirement on NVIDIA\textsuperscript{\textregistered} JETSON AGX Xavier with 19 frames per second in average.