Efficient Large-Scale Point Cloud Geometry Compression

Abstract

Due to the significant bandwidth and memory requirements for transmitting and storing large-scale point clouds, considerable progress has been made in recent years in the field of large-scale point cloud geometry compression. However, challenges remain, including suboptimal compression performance and complex encoding–decoding processes. To address these issues, we propose an efficient large-scale scene point cloud geometry compression algorithm. By analyzing the sparsity of large-scale point clouds and the impact of scale on feature extraction, we design a cross-attention module in the encoder to enhance the extracted features by incorporating positional information. During decoding, we introduce an efficient generation module that improves decoding quality without increasing decoding time. Experiments on three public datasets demonstrate that, compared to the state-of-the-art G-PCC v23, our method achieves an average bitrate reduction of −46.64%, the fastest decoding time, and a minimal network model size of 2.8 M.