用扩散模型实现低比特率点云压缩，兼顾高效与保真
Denoising Diffusion Probabilistic Model for Point Cloud Compression at Low Bit-Rates
- 基于点云的扩散生成模型，通过编码器+向量量化降低码率
- 在ShapeNet和ModelNet40上实现比现有方法更低的比特率
- 适合对传输带宽敏感的3D点云应用，如远程协作与移动设备
低比特率点云压缩对带宽受限的应用至关重要。然而，现有方法主要关注高保真重建，需大量比特。本文提出一种针对低比特率点云压缩的去噪扩散概率模型（DDPM-PCC）。采用PointNet编码器生成生成条件向量，并通过可学习的向量量化器进行量化，从而在保持质量的同时实现低比特率。在ShapeNet和ModelNet40上的实验表明，相比标准及前沿方法，该方法在低码率下具有更优的速率-失真性能。代码已公开于https://github.com/EIDOSLAB/DDPM-PCC。
原文摘要 · Abstract (English)
Efficient compression of low-bit-rate point clouds is critical for bandwidth-constrained applications. However, existing techniques mainly focus on high-fidelity reconstruction, requiring many bits for compression. This paper proposes a "Denoising Diffusion Probabilistic Model" (DDPM) architecture for point cloud compression (DDPM-PCC) at low bit-rates. A PointNet encoder produces the condition vector for the generation, which is then quantized via a learnable vector quantizer. This configuration allows to achieve a low bitrates while preserving quality. Experiments on ShapeNet and ModelNet40 show improved rate-distortion at low rates compared to standardized and state-of-the-art approaches. We publicly released the code at https://github.com/EIDOSLAB/DDPM-PCC.
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