This repo contains the implementation of our paper:

SegNet4D: Effective and Efficient 4D LiDAR Semantic Segmentation in Autonomous Driving Environments

Neng Wang, Ruibin Guo, Chenghao Shi, Hui Zhang, Huimin Lu, Zhiqiang Zheng, Xieyuanli Chen

SegNet4D is an effective and efficient 4D LiDAR semantic segmentation framework.

4D LiDAR semantic segmentation, also referred to as multi-scan semantic segmentation, plays a crucial role in enhancing the environmental understanding capabilities of autonomous vehicles. It entails identifying the semantic category of each point in the LiDAR scan and distinguishing whether it is dynamic, a critical aspect in downstream tasks such as path planning and autonomous navigation. Existing methods for 4D semantic segmentation often rely on computationally intensive 4D convolutions for multi-scan input, resulting in poor real-time performance. In this article, we introduce SegNet4D, a novel real-time multi-scan semantic segmentation method leveraging a projection-based approach for fast motion feature encoding, showcasing outstanding performance. SegNet4D treats 4D semantic segmentation as two distinct tasks: single-scan semantic segmentation and moving object segmentation, each addressed by dedicated head. These results are then fused in the proposed motion-semantic fusion module to achieve comprehensive multi-scan semantic segmentation. Besides, we propose extracting instance information from the current scan and incorporating it into the network for instance-aware segmentation. Our approach exhibits state-of-the-art performance across multiple datasets and stands out as a real-time multi-scan semantic segmentation method.

The code will be released after our paper is accepted.

Any question or suggestions are welcome!

Neng Wang: [email protected] and Xieyuanli Chen: [email protected]