Point cloud completion method based on attention mechanism

The invention discloses a point cloud completion method based on an attention mechanism, and the method comprises the steps: firstly obtaining a data set needed by a point cloud completion algorithm, feeding an incomplete point cloud into an encoder, carrying out the extraction, obtaining a feature vector of the incomplete point cloud, feeding the feature vector into a decoder, and generating a complete point cloud; performing quality evaluation on the generated point cloud by using the chamfering distance between the generated point cloud and the real point cloud to serve as a loss function to guide updating of neural network parameters; and finally, sending the actually scanned point cloud data into the trained encoder and decoder to generate a complete point cloud. The AGFA module is introduced to perform feature extraction on the existing geometric structure, the authenticity and better detail structure of the complemented point cloud shape are ensured, the CGFA module is adopted, the features of the prep