Stable diffusion networks have emerged as a groundbreaking development for their ability to produce realistic and detailed visual content. This characteristic renders them ideal decoders, capable of producing high-quality and aesthetically pleasing reconstructions. In this paper, we introduce the first diffusion-based point cloud compression method, dubbed Diff-PCC, to leverage the expressive power of the diffusion model for generative and aesthetically superior decoding. Different from the conventional autoencoder fashion, a dual-space latent representation is devised in this paper, in which a compressor composed of two independent encoding backbones is considered to extract expressive shape latents from distinct latent spaces. At the decoding side, a diffusion-based generator is devised to produce high-quality reconstructions by considering the shape latents as guidance to stochastically denoise the noisy point clouds. Experiments demonstrate that the proposed Diff-PCC achieves state-of-the-art compression performance (e.g., 7.711 dB BD-PSNR gains against the latest G-PCC standard at ultra-low bitrate) while attaining superior subjective quality. Source code will be made publicly available.

本研究解决了传统点云压缩方法在质量和压缩性能上的不足，提出了一种新颖的基于扩散的点云压缩方法Diff-PCC。该方法利用双空间潜在表示，通过独立编码骨干提取形状潜在信息，并通过扩散生成器实现高质量重建，实验结果显示其在超低比特率下实现了领先的压缩性能和优越的主观质量。

Diff-PCC：基于扩散的3D点云神经压缩