Deep point cloud compression coding method based on full self-attention network

The invention discloses a depth point cloud compression coding method based on a full-self-attention network, and the method comprises the steps: constructing a point cloud full-self-attention network which comprises an encoder and a decoder; obtaining training data, and constructing a chamfering distance objective function to train the point cloud full-self-attention network; inputting point cloud data into the trained point cloud full-self-attention network, performing feature sampling processing on the point cloud data by using an encoder to obtain a point cloud code, and completing point cloud compression; and reconstructing point cloud data by using a decoder according to the point cloud code to complete point cloud decompression. According to the method, learning of local and global correlation among points of the point cloud is enhanced based on network training of a chamfering distance objective function, point cloud codes capable of accurately representing semantic information of the point cloud are