ProDAT: Progressive Density-Aware Tail-Drop for Point Cloud Coding
This addresses bandwidth constraints for real-time applications like autonomous driving and augmented reality by enabling efficient progressive coding, though it is an incremental improvement over existing learning-based methods.
The paper tackles the problem of progressive point cloud coding by proposing ProDAT, a density-aware tail-drop mechanism that enables decoding at multiple bitrates with a single model, achieving over 28.6% BD-rate improvement on SemanticKITTI and over 18.15% on ShapeNet compared to state-of-the-art methods.
Three-dimensional (3D) point clouds are becoming increasingly vital in applications such as autonomous driving, augmented reality, and immersive communication, demanding real-time processing and low latency. However, their large data volumes and bandwidth constraints hinder the deployment of high-quality services in resource-limited environments. Progres- sive coding, which allows for decoding at varying levels of detail, provides an alternative by allowing initial partial decoding with subsequent refinement. Although recent learning-based point cloud geometry coding methods have achieved notable success, their fixed latent representation does not support progressive decoding. To bridge this gap, we propose ProDAT, a novel density-aware tail-drop mechanism for progressive point cloud coding. By leveraging density information as a guidance signal, latent features and coordinates are decoded adaptively based on their significance, therefore achieving progressive decoding at multiple bitrates using one single model. Experimental results on benchmark datasets show that the proposed ProDAT not only enables progressive coding but also achieves superior coding efficiency compared to state-of-the-art learning-based coding techniques, with over 28.6% BD-rate improvement for PSNR- D2 on SemanticKITTI and over 18.15% for ShapeNet