SegNet4D: Efficient Instance-Aware 4D Semantic Segmentation for LiDAR Point Cloud
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913595641561088 |
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| author | Wang, Neng Guo, Ruibin Shi, Chenghao Wang, Ziyue Zhang, Hui Lu, Huimin Zheng, Zhiqiang Chen, Xieyuanli |
| author_facet | Wang, Neng Guo, Ruibin Shi, Chenghao Wang, Ziyue Zhang, Hui Lu, Huimin Zheng, Zhiqiang Chen, Xieyuanli |
| contents | 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 or 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. Our code will be released at https://github.com/nubot-nudt/SegNet4D. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_16279 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SegNet4D: Efficient Instance-Aware 4D Semantic Segmentation for LiDAR Point Cloud Wang, Neng Guo, Ruibin Shi, Chenghao Wang, Ziyue Zhang, Hui Lu, Huimin Zheng, Zhiqiang Chen, Xieyuanli Computer Vision and Pattern Recognition 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 or 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. Our code will be released at https://github.com/nubot-nudt/SegNet4D. |
| title | SegNet4D: Efficient Instance-Aware 4D Semantic Segmentation for LiDAR Point Cloud |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2406.16279 |