Submanifold Convolution

2000년 도입 · 논문 7편에서 사용

Submanifold Convolution (SC) is a spatially sparse 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, or the official code here.

출처: 3D Semantic Segmentation with Submanifold Sparse Convolutional Networks

소개 논문: 3D Semantic Segmentation with Submanifold Sparse Convolutional Networks