Multi Scale Sparse Convolution Point Cloud Semantic Segmentation Neural Network

Point clouds have the characteristics of disorder, unstructured and sparseness.Aiming at the problem of the non-structural nature of point clouds, thanks to the excellent performance of convolutional neural networks in image processing, one of the solutions is to extract features from point clouds based on two-dimensional convolutional neural networks. The three-dimensional information carried in the point cloud can be converted to two-dimensional, and then processed by a two-dimensional convolutional neural network, and finally back-projected to three-dimensional.In the process of projecting 3D information to 2D and back-projection, certain information loss will inevitably be caused to the point cloud and category inconsistency will be introduced in the back-projection stage;Another solution is the voxel-based point cloud segmentation method, which divides the point cloud into small grids one by one.However, the point cloud is sparse, and the direct use of 3D convolutional neural network inevitably wastes computing resources. In this paper, we propose a feature extraction module based on multi-scale ultra-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, network could capture richer feature information based on convolution kernels of different sizes, improving the segmentation result of point cloud segmentation.