The invention discloses a photogrammetry point cloud semantic segmentation method based on deep learning. The method comprises the following steps: firstly, designing a group of data enhancement methods for original point cloud to improve the generalization ability of limited training data; then dynamically updating a point cloud local neighborhood graph at each layer of the network, combining cross-layer point description with context features, and converging point cloud space potential semantic information layer by layer; and finally, introducing a channel attention mechanism into each network layer to enable the network to adaptively learn the weight between channels, thereby establishing a point cloud semantic segmentation network based on a lightweight channel attention pool, and realizing fine-grained semantic segmentation of the complex point cloud. The photogrammetry point cloud semantic segmentation method based on deep learning provided by the invention is lightweight, sensitive in spatial relationship and good in universality.
Photogrammetry point cloud semantic segmentation method based on deep learning