Neural Compression System for Point Cloud Video Streaming

Abstract

Point cloud video streaming is promising for immersive media applications, which urges the development of efficient compression methods. However, existing approaches either suffer from poor performance or lack effective coder control mechanisms, making them impractical for networked point cloud services, where bandwidth is often constrained and fluctuates over time. Therefore, this paper proposes a system-level solution - a layered point cloud compressor, called Yak, to address these issues. Yak offers comprehensive support for both intra and inter-frame coding of geometry and attribute components in point cloud sequences. It consists of three layers: the Base Layer uses the standard G-PCC to encode a thumbnail counterpart downscaled from the input point cloud; the Enhancement Layer devises the end-to-end variational autoencoder to compress the original input conditioned on the base layer reconstruction, and the Dynamic Layer generates feature-space predictions as the temporal prior for conditional inter-frame coding. In addition, Yak devises the Content Analysis module to dynamically determine the optimal encoding parameters of each frame, by which bits budget is intelligently allocated for geometry and attribute components to maximize the overall rate-distortion (R-D) performance. Such accurate rate control relies on the parametric rate/distortion models whose parameters are initialized through one-pass template matching and frame-wise delta updating constrained by R-D optimization. Following standard evaluation guidelines, Yak has notably outperformed traditional rules-based methods such as MPEG G-PCC and V-PCC, as well as other learning-based approaches, while offering flexible networked adaption and affordable complexity.