# SPNet: Multi-Shell Kernel Convolution for Point Cloud Semantic Segmentation Source: [SPNet: Multi-Shell Kernel Convolution for Point Cloud Semantic Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:56:49.888Z Authors: Yuyan Li, Chuanmao Fan, Xu Wang, Ye Duan Published: 2021-09-23T19:34:20.000Z ## Abstract Feature encoding is essential for point cloud analysis. In this paper, we propose a novel point convolution operator named Shell Point Convolution (SPConv) for shape encoding and local context learning. Specifically, SPConv splits 3D neighborhood space into shells, aggregates local features on manually designed kernel points, and performs convolution on the shells. Moreover, SPConv incorporates a simple yet effective attention module that enhances local feature aggregation. Based upon SPConv, a deep neural network named SPNet is constructed to process large-scale point clouds. Poisson disk sampling and feature propagation are incorporated in SPNet for better efficiency and accuracy. We provided details of the shell design and conducted extensive experiments on challenging large-scale point cloud datasets. Experimental results show that SPConv is effective in local shape encoding, and our SPNet is able to achieve top-ranking performances in semantic segmentation tasks. ## 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]()