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GTNet: Graph Transformer Network for 3D Point Cloud Classification and Semantic Segmentation
Local Transformer uses a dynamic graph to calculate all neighboring point weights by intra-domain cross-attention with dynamically updated graph relations, so that every neighboring point could affect the features of centroid with different weights; Global Transformer enlarges the receptive field of Local Transformer by a global self-attention.
VTPNet for 3D deep learning on point cloud
Recently, Transformer-based methods for point cloud learning have achieved good results on various point cloud learning benchmarks.
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