Variable-Rate Point Cloud Geometry Compression Based on Feature Adjustment and Interpolation

Zhiyang Qi, Wei Gao

Abstract: Learning-based point cloud compression technology based on voxel structure has made significant progress in recent years. However, these methods have to train multiple models for different bit rates, which consume more storage and training resources. In a limited bit rate environment, these methods can only choose one model from a finite number of pretrained models, resulting in bit waste and low compression efficiency. To tackle these difficulties, we propose a variable-rate point cloud geometry compression network. A channel feature adjustment module (CFA) is designed to regulate features and achieve variable bit rates within one model. Then, in order to compress the point cloud to any bit rate, we also propose feature and entropy parameters interpolation methods. The proposed framework can realize a fine rate interval of 0.0001 bits per point (BPP) in point cloud compression. The experiment results demonstrate that the variable-rate network shows comparable performance to the other voxel based fixed-rate point cloud compression methods.