Submanifold Convolution
Submanifold Convolution
**Submanifold Convolution (SC)** is a spatially sparse [convolution](https://paperswithcode.com/method/convolution) operation used for tasks with sparse data like semantic segmentation of 3D point clouds. An SC convolution computes the set of active sites in the same way as a regular convolution: it looks for the presence of any active sites in its receptive field of size $f^{d}$. If the input has size $l$ then the output will have size $\left(l − f + s\right)/s$. Unlike a regular convolution, an SC convolution discards the ground state for non-active sites by assuming that the input from those sites is zero. For more details see the [paper](https://paperswithcode.com/paper/3d-semantic-segmentation-with-submanifold), or the official code [here](https://github.com/facebookresearch/SparseConvNet).
3D Semantic Segmentation with Submanifold Sparse Convolutional Networks
https://paperswithcode.com/paper/3d-semantic-segmentation-with-submanifold
3D Semantic Segmentation with Submanifold Sparse Convolutional Networks