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.

翻译：近年来,随着计算资源的开发以及LIDAR的开发,点云的语义分割吸引了许多研究人员。对于点云的广度,尽管已经存在处理稀疏变异的方法,但并未考虑多尺度特征。在本信中,我们提议了一个基于多尺度稀疏变异的特征提取模块和一个基于频道注意力的特征选择模块,并在此基础上建立了一个点云分割网络框架。通过引入多尺度变异变异,该网络可以捕捉到基于不同大小的变异内核的更丰富的特征信息,改善点云分离的分解结果。斯坦福大型三维室内空间(S3Dis)数据集和室外数据集的实验结果证明了拟议模型的有效性和优越性。