DiffCom: Decoupled Sparse Priors Guided Diffusion Compression for Point Clouds

While conventional lossy compression methods predominantly depend on autoencoders to map point clouds into latent representations, they often neglect the intrinsic redundancy within these latent points. To address this limitation, this paper presents a diffusion-based architecture steered by sparse priors, designed to minimize latent redundancy while securing superior reconstruction fidelity, particularly in low-bitrate scenarios. A key feature of the framework is an efficient dual-density data flow that alleviates the stringent size constraints imposed on latent points. By integrating a Probabilistic Attention-based Conditional Denoiser (PACD), the method effectively encapsulates critical reconstruction details within sparse priors, which are hierarchically decoupled into intra- and inter-point components. Specifically, separate encoders are utilized to transform the source point cloud into latent points and decoupled sparse priors, respectively. To dynamically exploit geometric and semantic information, an attention-driven latent denoiser, conditioned on these decoupled priors, is applied across the encoding and decoding layers. Furthermore, inter-point distributions are incorporated into the arithmetic codec to refine local context modeling for sparse points, with the final point cloud recovered via a point decoder. Comprehensive experiments conducted on ShapeNet and standard MPEG PCC datasets demonstrate that the proposed method outperforms state-of-the-art techniques, achieving a superior rate-distortion trade-off.