TL;DR: 4D LiDAR semantic segmentation, also referred to as multi-scan semantic segmentation, plays a crucial role in enhancing the environmental understanding capabilities of autonomous robots. It classifies the semantic category of each LiDAR measurement point and detects whether it is dynamic, a critical ability for tasks like obstacle avoidance and autonomous navigation.
Existing approaches often rely on computationally heavy 4D convolutions or recursive networks, which result in poor real-time performance, making them unsuitable for online robotics and autonomous driving applications.
In this paper, we introduce SegNet4D, a novel real-time 4D semantic segmentation network offering both efficiency and strong semantic understanding. SegNet4D addresses 4D segmentation as two tasks: single-scan semantic segmentation and moving object segmentation, each tackled by a separate network head. Both results are combined in a motion-semantic fusion module to achieve comprehensive 4D segmentation. Additionally, instance information is extracted from the current scan and exploited for instance-wise segmentation consistency.
Our approach surpasses state-of-the-art in both multi-scan semantic segmentation and moving object segmentation while offering greater efficiency, enabling real-time operation.
Besides, its effectiveness and efficiency have also been validated on a real-world unmanned ground platform.
@article{wang2024segnet4d,
title = {{SegNet4D: Efficient Instance-Aware 4D Semantic Segmentation for LiDAR Point Cloud}},
author = {Neng, Wang and Ruibin, Guo and Chenghao, Shi and Ziyue, Wang and Hui, Zhang and Huimin, Lu and Zhiqiang, Zheng and Xieyuanali, Chen},
journal = {arXiv},
year = {2024}
}