mlsanityData to 14 Oct 2026

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

arXiv 2505.13316 ↗First posted 19 May 2025cs.CV cs.AI cs.LGcited by 0

On the map: Diffusion models → 3D, point cloud and scene generation

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in 3D, point cloud and scene generation, one of 14 directions in Diffusion models. Builds on 21 of its 26 references that are in the map. First posted on arXiv 19 May 2025.

Builds on

21 of 26 references.

Closest ancestors · 1

Same direction, newest first

  1. 2021Diffusion Probabilistic Models for 3D Point Cloud Generation2 Mar

Other references · 20

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  3. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  4. 2017Neural Discrete Representation Learningcited by 4,712
  5. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,401
  6. 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,897
  7. 2016End-to-end Optimized Image Compressioncited by 696
  8. 2023Learned Image Compression with Mixed Transformer-CNN Architecturescited by 211
  9. 2022Lossy Image Compression with Conditional Diffusion Modelscited by 170
  10. 2023Towards image compression with perfect realism at ultra-low bitratescited by 103
  11. 2020Multiscale Point Cloud Geometry Compressioncited by 74
  12. 2021VoxelContext-Net: An Octree based Framework for Point Cloud Compressioncited by 61
8 more
  1. 2019Learned Point Cloud Geometry Compressioncited by 58
  2. 2022OctAttention: Octree-Based Large-Scale Contexts Model for Point Cloud Compressioncited by 58
  3. 2019Learning Convolutional Transforms for Lossy Point Cloud Geometry Compressioncited by 54
  4. 2020Improved Deep Point Cloud Geometry Compressioncited by 43
  5. 2022Density-preserving Deep Point Cloud Compressioncited by 39
  6. 2019Deep AutoEncoder-based Lossy Geometry Compression for Point Cloudscited by 29
  7. 20143D ShapeNets: A Deep Representation for Volumetric Shapescited by 27
  8. 2024Point Cloud Compression via Constrained Optimal Transportcited by 3

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  1. 2024Diff-PCC: Diffusion-based Neural Compression for 3D Point Clouds
  2. 2024DiffCom: Decoupled Sparse Priors Guided Diffusion Compression for Point Clouds
  3. 2023A Conditional Denoising Diffusion Probabilistic Model for Point Cloud Upsampling
  4. 2025Adaptive and Iterative Point Cloud Denoising with Score-Based Diffusion Model
  5. 2025Turbo-DDCM: Fast and Flexible Zero-Shot Diffusion-Based Image Compression
  6. 2025InJecteD: Analyzing Trajectories and Drift Dynamics in Denoising Diffusion Probabilistic Models for 2D Point Cloud Generation
  7. 2025Compressed Image Generation with Denoising Diffusion Codebook Models
  8. 2024Denoising Diffusion Probabilistic Models in Six Simple Steps