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arXiv · 2505.13316

Denoising Diffusion Probabilistic Model for Point Cloud Compression at Low Bit-Rates

Gabriele Spadaro·Alberto Presta·Jhony H. Giraldo·Marco Grangetto·Wei Hu·Giuseppe Valenzise·Attilio Fiandrotti·Enzo Tartaglione
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Abstract

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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Gabriele Spadaro, Alberto Presta, Jhony H. Giraldo, Marco Grangetto, Wei Hu, Giuseppe Valenzise, Attilio Fiandrotti, Enzo Tartaglione. 2025-05-19. Denoising Diffusion Probabilistic Model for Point Cloud Compression at Low Bit-Rates. https://arxiv.org/abs/2505.13316

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