A part model feature recognition method combining attribute adjacency graph and point cloud
A part model feature recognition method combining attribute adjacency graph and point cloud was proposed by combi-ning two feature recognition methods to overcome the limitations of current part model feature recognition technology based on at-tribute adjacency graph and point cloud.The model attribute adjacency graph was used to match feature subgraphs to find and sep-arate feature surfaces,and then the feature surfaces in point clouds were sampled.The point cloud classification network structure on the basis of PointNet network was improved by adding a local feature extraction module and a Transformer based non-local fea-ture extraction module and combining feature attribute adjacency graph information with original point cloud data.Experimental re-sults indicate that the recognition accuracy for 24 common features is 99.92％.
part model feature recognitionattribute adjacency graphpoint cloudTransformer netPointNet
国家科技期刊平台
NSTL
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