Abstract: Existing point cloud compression methods based on deep learning require training multiple networks, which consumes a large amount of time and space resources. To address this issue, a variable rate point cloud compression network model based on channel gain is proposed. First, the encoding end of the network uses a hierarchical structure to extract point cloud features and apply offset attention mechanisms at each level, effectively capturing the key feature information of the input point cloud. Secondly, gain units are introduced to evaluate and scale the channel of each hidden vector, eliminating information redundancy between vector channels, and requiring only one network to achieve variable rate compression. Then, to predict the probability distribution of feature vectors, a hyperprior encoder is performed on the feature vectors to construct a Gaussian entropy model, further reducing the coding volume through entropy coding. Finally, the decoder uses sub-point convolutions for upsampling to reconstruct the original point cloud, avoiding local point aggregation and improving the reconstruction quality. For the ShapeNet dataset, with BD-rate on the rate-distortion curve as the performance evaluation index, our method reduces the average bitrate by 48.66% and 63.56% compared with VRR and Draco methods, respectively. The experimental results show that the compression performance has been significantly improved compared to current methods.