Papers › ShellNet: Efficient Point Cloud Convolutional Neural Networks using Concentric Shells...
ShellNet: Efficient Point Cloud Convolutional Neural Networks using Concentric Shells Statistics
Zhiyuan Zhang, Binh-Son Hua, Sai-Kit Yeung
Deep learning with 3D data has progressed significantly since the introduction of convolutional neural networks that can handle point order ambiguity in point cloud data. While being able to achieve good accuracies in various scene understanding tasks, previous methods often have low training speed and complex network architecture. In this paper, we address these problems by proposing an efficient end-to-end permutation invariant convolution for point cloud deep learning. Our simple yet effective convolution operator named ShellConv uses statistics from concentric spherical shells to define representative features and resolve the point order ambiguity, allowing traditional convolution to perform on such features. Based on ShellConv we further build an efficient neural network named ShellNet to directly consume the point clouds with larger receptive fields while maintaining less layers. We demonstrate the efficacy of ShellNet by producing state-of-the-art results on object classification, object part segmentation, and semantic scene segmentation while keeping the network very fast to train.
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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 Point Cloud Classification | ModelNet40 | ShellNet | Overall Accuracy | 93.1 | #72 of 111 | Archive leaderboard | report |
| 3D Semantic Segmentation | DALES | ShellNet | Model size | N/A | #9 of 9 | Archive leaderboard | report |
| 3D Semantic Segmentation | DALES | ShellNet | Overall Accuracy | 96.4 | #9 of 9 | Archive leaderboard | report |
| 3D Semantic Segmentation | DALES | ShellNet | mIoU | 57.4 | #9 of 9 | Archive leaderboard | report |
| Semantic Segmentation | S3DIS | ShellNet | Mean IoU | 66.8 | #33 of 54 | Archive leaderboard | report |
| Semantic Segmentation | S3DIS | ShellNet | Number of params | N/A | #33 of 54 | Archive leaderboard | report |
| Semantic Segmentation | Semantic3D | shellnet_v2 | mIoU | 69.3% | #10 of 17 | 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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