SegNet4D: Effective and Efficient 4D LiDAR Semantic Segmentation in Autonomous Driving Environments
<|reference_start|>SegNet4D: Effective and Efficient 4D LiDAR Semantic Segmentation in Autonomous Driving Environments: 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 \url{https://github.com/nubot-nudt/SegNet4D}.<|reference_end|>
@article{wang2024segnet4d:,
    title={SegNet4D: Efficient Instance-Aware 4D LiDAR Semantic Segmentation for
  Driving Scenarios},
    author={Neng Wang and Ruibin Guo and Chenghao Shi and Ziyue Wang and Hui Zhang
  and Huimin Lu and Zhiqiang Zheng and Xieyuanli Chen},
    journal={arXiv preprint arXiv:2406.16279},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.16279},
    primaryClass={cs.CV}
}