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 implementation of
SegNet4D will be made available at
https://github.com/nubot-nudt/SegNet4D.

4D LiDAR 语义分割（也称为多扫描语义分割）是增强自动驾驶车辆环境理解能力的关键。本文介绍了 SegNet4D，一种利用基于投影的方法进行快速动态特征编码的实时多扫描语义分割方法，展示了卓越的性能。SegNet4D 将 4D 语义分割视为两个不同的任务：单扫描语义分割和运动目标分割，分别由专用头部解决，然后在提出的运动 - 语义融合模块中进行融合以实现全面的多扫描语义分割。此外，我们提出从当前扫描中提取实例信息，并将其并入网络以实现实例感知分割。我们的方法在多个数据集上表现出卓越的性能，是一种实时多扫描语义分割方法。SegNet4D 的实现将在 https://github.com/nubot-nudt/SegNet4D 上提供。