Papers › Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds
Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds
Huan Lei, Naveed Akhtar, Ajmal Mian
We propose a spherical kernel for efficient graph convolution of 3D point clouds. Our metric-based kernels systematically quantize the local 3D space to identify distinctive geometric relationships in the data. Similar to the regular grid CNN kernels, the spherical kernel maintains translation-invariance and asymmetry properties, where the former guarantees weight sharing among similar local structures in the data and the latter facilitates fine geometric learning. The proposed kernel is applied to graph neural networks without edge-dependent filter generation, making it computationally attractive for large point clouds. In our graph networks, each vertex is associated with a single point location and edges connect the neighborhood points within a defined range. The graph gets coarsened in the network with farthest point sampling. Analogous to the standard CNNs, we define pooling and unpooling operations for our network. We demonstrate the effectiveness of the proposed spherical kernel with graph neural networks for point cloud classification and semantic segmentation using ModelNet, ShapeNet, RueMonge2014, ScanNet and S3DIS datasets. The source code and the trained models can be downloaded from https://github.com/hlei-ziyan/SPH3D-GCN.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Object Classification | ModelNet40 | Spherical Kernel | Classification Accuracy | 89.3 | #6 of 7 | Archive leaderboard | report |
| 3D Part Segmentation | ShapeNet-Part | Spherical Kernel | Class Average IoU | 84.9 | #13 of 67 | Archive leaderboard | report |
| 3D Part Segmentation | ShapeNet-Part | Spherical Kernel | Instance Average IoU | 86.8 | #13 of 67 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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