  In recent years, with the development of computing resources and LiDAR, point
cloud semantic segmentation has attracted many researchers. For the sparsity of
point clouds, although there is already a way to deal with sparse convolution,
multi-scale features are not considered. In this letter, we propose a feature
extraction module based on multi-scale sparse convolution and a feature
selection module based on channel attention and build a point cloud
segmentation network framework based on this. By introducing multi-scale sparse
convolution, the network could capture richer feature information based on
convolution kernels with different sizes, improving the segmentation result of
point cloud segmentation. Experimental results on Stanford large-scale 3-D
Indoor Spaces(S3DIS) dataset and outdoor dataset(SemanticKITTI), demonstrate
effectiveness and superiority of the proposed mothod.
