Papers › SPLATNet: Sparse Lattice Networks for Point Cloud Processing
SPLATNet: Sparse Lattice Networks for Point Cloud Processing
Hang Su, Varun Jampani, Deqing Sun, Subhransu Maji, Evangelos Kalogerakis, Ming-Hsuan Yang, Jan Kautz
We present a network architecture for processing point clouds that directly operates on a collection of points represented as a sparse set of samples in a high-dimensional lattice. Naively applying convolutions on this lattice scales poorly, both in terms of memory and computational cost, as the size of the lattice increases. Instead, our network uses sparse bilateral convolutional layers as building blocks. These layers maintain efficiency by using indexing structures to apply convolutions only on occupied parts of the lattice, and allow flexible specifications of the lattice structure enabling hierarchical and spatially-aware feature learning, as well as joint 2D-3D reasoning. Both point-based and image-based representations can be easily incorporated in a network with such layers and the resulting model can be trained in an end-to-end manner. We present results on 3D segmentation tasks where our approach outperforms existing state-of-the-art techniques.
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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 Part Segmentation | ShapeNet-Part | SPLATNet 3D | Class Average IoU | 82.0 | #59 of 67 | Archive leaderboard | report |
| 3D Part Segmentation | ShapeNet-Part | SPLATNet 3D | Instance Average IoU | 84.6 | #59 of 67 | Archive leaderboard | report |
| 3D Semantic Segmentation | SemanticKITTI | SPLATNet | test mIoU | 18.4% | #39 of 45 | Archive leaderboard | report |
| Semantic Segmentation | ScanNet | SPLAT Net | test mIoU | 39.3 | #44 of 45 | 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.
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