Dynamic point cloud compression with spatio-temporal transformer-style modeling

The essence of dynamic point cloud compression lies in the effective modeling of temporal context information, which poses significant challenges owing to the unstructured and sparse characteristics of point clouds. Existing dynamic compression methods exhibit a limited capacity to capture and leverage inter-frame information. Consequently, in this paper, we propose a Dynamic Point Cloud Compression framework with Spatio-Temporal Transformer-style Modeling (DPCC-STTM) to compress point cloud sequences within a latent space. To effectively extract and fully utilize temporal context, we introduce a spatio-temporal transformer-style modeling module, which performs effective modeling of the rich temporal information based on the correlation of temporal content. Furthermore, we introduce a multi-scale temporal processing module that captures temporal correlations across short and long ranges of multi-frame point clouds. This module also fuses modeled temporal information to enhance the prediction accuracy of potential features for the current frame. Extensive experiments demonstrate the superiority of our proposed framework, validated through both objective evaluation and subjective perception.