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

• URL: http://arxiv.org/abs/2505.13316v1
• Date: Mon, 19 May 2025 16:29:12 GMT
• Title: Denoising Diffusion Probabilistic Model for Point Cloud Compression at Low Bit-Rates
• Abstract summary: This paper proposes a "Denoising Diffusion Probabilistic Model" architecture for point cloud compression.<n>A PointNet encoder produces the condition vector for the generation, which is then quantized via a learnable vector quantizer.<n> Experiments on ShapeNet and ModelNet40 show improved rate-distortion at low rates compared to standardized and state-of-the-art approaches.
• Score: 22.076896401919683
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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