# NUC-Net: Non-uniform Cylindrical Partition Network for Efficient LiDAR Semantic Segmentation Source: [NUC-Net: Non-uniform Cylindrical Partition Network for Efficient LiDAR Semantic Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-26T04:50:02.046Z Authors: Xuzhi Wang, Wei Feng, Lingdong Kong, Liang Wan Published: 2025-05-30T14:25:32.000Z ## 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}. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()