Semantic segmentation of 3D point cloud based on boundary point estimation and sparse convolution neural network
The large-scale point clouds are sparse,the traditional point cloud methods are insufficient in extracting rich contextual semantic features,and the semantic segmentation results have the problem of fuzzy object boundaries.A 3D point cloud semantic segmentation algorithm based on boundary point estimation and sparse convolution neural network was proposed,mainly including the voxel branch and the point branch.For the voxel branch,the original point cloud was voxelized,and then the contextual semantic features were obtained by sparse convolution.The initial semantic label of each point was obtained by voxelization.Finally,it was input into the boundary point estimation module to get the possible boundary points.For the point branch,the improved dynamic graph convolution module was first used to extract the local geometric features of the point cloud.Then,the local features were enhanced through the spatial attention module and the channel attention module in turn.Finally,the local geometric features obtained from the point branch and the contextual features obtained from the voxel branch were fused to enhance the richness of point cloud features.The semantic segmentation accuracy values of this algorithm on the S3DIS dataset and SemanticKITTI dataset were 69.5％and 62.7％,respectively.Experimental results show that the proposed algorithm can extract richer features of point clouds,accurately segment object boundary regions,and has good semantic segmentation ability for 3D point clouds.
point cloud datasemantic segmentationattention mechanismsparse convolutionvoxelization
万方数据
维普
