# SegNet4D: Efficient Instance-Aware 4D Semantic Segmentation for LiDAR Point Cloud

Source: [SegNet4D: Efficient Instance-Aware 4D Semantic Segmentation for LiDAR Point Cloud](<https://www.sota2.com/research/paper/68fa75f51e209131cea6aa7b>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T03:44:34.944Z

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

Published: 2024-06-24T03:01:08.000Z

## Abstract

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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2406.16279>)

[PDF](<https://arxiv.org/pdf/2406.16279.pdf>)