Context-Adaptive Entropy Model With Adapters For Lossless Point Cloud Geometry Compression

Learning-based point cloud compression has achieved tremendous progress in recent years. However, existing methods often train an optimal occupancy distribution predictor for the entire train dataset in an amortization sense, which struggles to handle point clouds with unique characteristics. In this work, we focus on the lossless point cloud compression, and propose a novel context-adaptive entropy model to achieve adaptive occupancy prediction. Specifically, given a baseline entropy model and a point cloud, we firstly integrate adapters into diverse feature extraction modules. These adapters are then trained to be specifically attuned to the input cloud. Finally, the trained adapter parameters are encoded and transmitted along with the point cloud bitstream, which allow us to recover the integrated model in decoder. The experimental results demonstrate that our method can enhance the performance of the entropy model, especially improving the compression performance of data that performs poorly in conventional methods.