NUC-Net: Non-uniform Cylindrical Partition Network for Efficient LiDAR Semantic Segmentation

• URL: http://arxiv.org/abs/2505.24634v2
• Date: Mon, 02 Jun 2025 02:51:49 GMT
• Title: NUC-Net: Non-uniform Cylindrical Partition Network for Efficient LiDAR Semantic Segmentation
• Authors: Xuzhi Wang, Wei Feng, Lingdong Kong, Liang Wan,
• Abstract summary: We propose a non-uniform cylindrical partition network named NUC-Net to tackle the challenges of LiDAR semantic segmentation.<n>Our method achieves state-of-the-art performance on Semantic KITTI and nuScenes datasets with much faster speed and much less training time.<n>Our method can be a general component for LiDAR semantic segmentation, which significantly improves both the accuracy and efficiency of the uniform counterpart by $4 times$ training faster and $2 times GPU memory reduction and $3 times$ inference speedup.
• Score: 17.280357264324376
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: LiDAR semantic segmentation plays a vital role in autonomous driving. Existing voxel-based methods for LiDAR semantic segmentation apply uniform partition to the 3D LiDAR point cloud to form a structured representation based on cartesian/cylindrical coordinates. Although these methods show impressive performance, the drawback of existing voxel-based methods remains in two aspects: (1) it requires a large enough input voxel resolution, which brings a large amount of computation cost and memory consumption. (2) it does not well handle the unbalanced point distribution of LiDAR point cloud. In this paper, we propose a non-uniform cylindrical partition network named NUC-Net to tackle the above challenges. Specifically, we propose the Arithmetic Progression of Interval (API) method to non-uniformly partition the radial axis and generate the voxel representation which is representative and efficient. Moreover, we propose a non-uniform multi-scale aggregation method to improve contextual information. Our method achieves state-of-the-art performance on SemanticKITTI and nuScenes datasets with much faster speed and much less training time. And our method can be a general component for LiDAR semantic segmentation, which significantly improves both the accuracy and efficiency of the uniform counterpart by $4 \times$ training faster and $2 \times$ GPU memory reduction and $3 \times$ inference speedup. We further provide theoretical analysis towards understanding why NUC is effective and how point distribution affects performance. Code is available at \href{https://github.com/alanWXZ/NUC-Net}{https://github.com/alanWXZ/NUC-Net}.

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