Methods › Computer Vision › Convolutions › ShapeConv
ShapeConv
Introduced by Jinming Cao et al. in ShapeConv: Shape-aware Convolutional Layer for Indoor RGB-D Semantic Segmentation
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
ShapeConv, or Shape-aware Convolutional layer, is a convolutional layer for processing the depth feature in indoor RGB-D semantic segmentation. The depth feature is firstly decomposed into a shape-component and a base-component, next two learnable weights are introduced to cooperate with them independently, and finally a convolution is applied on the re-weighted combination of these two components.
Papers archive 2025-07-28
2 shown of 2, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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ShapeMoiré: Channel-Wise Shape-Guided Network for Image Demoiréing 28 Apr 2024 · 1 repository · arXiv:2404.18155
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ShapeConv: Shape-aware Convolutional Layer for Indoor RGB-D Semantic Segmentation 24 Aug 2021 · 1 repository · arXiv:2108.10528Syntology ran 0 of 4 samples · 4 unverified
Tasks archive 2025-07-28
3 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Segmentation | 1 |
| Semantic Segmentation | 1 |
| Thermal Image Segmentation | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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