提出一种自适应密度引导的渐进式点云编码方法，支持多码率解码。
ProDAT: Progressive Density-Aware Tail-Drop for Point Cloud Coding
- 根据点云密度动态裁剪低重要性特征与坐标，实现渐进解码
- 在SemanticKITTI上比最优方法提升28.6%的BD-rate性能
- 单模型支持多码率输出，适合资源受限场景部署
三维点云在自动驾驶、增强现实和沉浸式通信等应用中日益重要，但其庞大的数据量和带宽限制制约了资源受限环境中的高质量服务部署。渐进编码允许在不同细节层级进行解码，通过初始部分解码并逐步细化，提供灵活的传输方案。尽管基于学习的点云几何编码方法已取得显著进展，但其固定的潜在表示无法支持渐进解码。为此，我们提出ProDAT，一种新型的密度感知尾部裁剪机制，用于渐进点云编码。该方法利用密度信息作为指导信号，自适应地解码具有不同重要性的潜在特征和坐标，从而仅用一个模型即可在多个码率下实现渐进解码。在基准数据集上的实验结果表明，所提出的ProDAT不仅实现了渐进编码，还相较于当前最先进的学习型编码技术，在语义点云数据集SemanticKITTI上达到超过28.6%的PSNR-D2 BD-rate提升，在ShapeNet上提升超过18.15%。
原文摘要 · Abstract (English)
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
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