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
• Authors: Gabriele Spadaro, Alberto Presta, Jhony H. Giraldo, Marco Grangetto, Wei Hu, Giuseppe Valenzise, Attilio Fiandrotti, Enzo Tartaglione,
• 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.

Related papers

• DiffO: Single-step Diffusion for Image Compression at Ultra-Low Bitrates [7.344746778324299]
  We propose the first single step diffusion model for image compression (DiffO) that delivers high perceptual quality and fast decoding at ultra lows.<n>Experiments show that DiffO surpasses state the art compression performance while improving decoding speed by 50x compared to prior diffusion-based methods.
  arXiv  Detail & Related papers  (2025-06-19T19:53:27Z)
• Reducing Storage of Pretrained Neural Networks by Rate-Constrained Quantization and Entropy Coding [56.066799081747845]
  The ever-growing size of neural networks poses serious challenges on resource-constrained devices.<n>We propose a novel post-training compression framework that combines rate-aware quantization with entropy coding.<n>Our method allows for very fast decoding and is compatible with arbitrary quantization grids.
  arXiv  Detail & Related papers  (2025-05-24T15:52:49Z)
• Multi-Scale Invertible Neural Network for Wide-Range Variable-Rate Learned Image Compression [90.59962443790593]
  In this paper, we present a variable-rate image compression model based on invertible transform to overcome limitations.<n> Specifically, we design a lightweight multi-scale invertible neural network, which maps the input image into multi-scale latent representations.<n> Experimental results demonstrate that the proposed method achieves state-of-the-art performance compared to existing variable-rate methods.
  arXiv  Detail & Related papers  (2025-03-27T09:08:39Z)
• Point Cloud Compression with Bits-back Coding [32.9521748764196]
  This paper specializes in using a deep learning-based probabilistic model to estimate the Shannon's entropy of the point cloud information. Once the entropy of the point cloud dataset is estimated, we use the learned CVAE model to compress the geometric attributes of the point clouds. The novelty of our method with bits-back coding specializes in utilizing the learned latent variable model of the CVAE to compress the point cloud data.
  arXiv  Detail & Related papers  (2024-10-09T06:34:48Z)
• SPAC: Sampling-based Progressive Attribute Compression for Dense Point Clouds [51.313922535437726]
  We propose an end-to-end compression method for dense point clouds. The proposed method combines a frequency sampling module, an adaptive scale feature extraction module with geometry assistance, and a global hyperprior entropy model.
  arXiv  Detail & Related papers  (2024-09-16T13:59:43Z)
• Point Cloud Compression via Constrained Optimal Transport [10.795619052889952]
  COT-PCC takes compressed features as an extra constraint of optimal transport. It learns the distribution transformation between original and reconstructed points. COT-PCC outperforms state-of-the-art methods in terms of both CD and PSNR metrics.
  arXiv  Detail & Related papers  (2024-03-13T04:36:24Z)
• Compression of Structured Data with Autoencoders: Provable Benefit of Nonlinearities and Depth [83.15263499262824]
  We prove that gradient descent converges to a solution that completely disregards the sparse structure of the input. We show how to improve upon Gaussian performance for the compression of sparse data by adding a denoising function to a shallow architecture. We validate our findings on image datasets, such as CIFAR-10 and MNIST.
  arXiv  Detail & Related papers  (2024-02-07T16:32:29Z)
• Activations and Gradients Compression for Model-Parallel Training [85.99744701008802]
  We study how simultaneous compression of activations and gradients in model-parallel distributed training setup affects convergence. We find that gradients require milder compression rates than activations. Experiments also show that models trained with TopK perform well only when compression is also applied during inference.
  arXiv  Detail & Related papers  (2024-01-15T15:54:54Z)
• Extreme Image Compression using Fine-tuned VQGANs [43.43014096929809]
  We introduce vector quantization (VQ)-based generative models into the image compression domain. The codebook learned by the VQGAN model yields a strong expressive capacity. The proposed framework outperforms state-of-the-art codecs in terms of perceptual quality-oriented metrics.
  arXiv  Detail & Related papers  (2023-07-17T06:14:19Z)
• Deep probabilistic model for lossless scalable point cloud attribute compression [2.2559617939136505]
  We build an end-to-end point cloud attribute coding method (MNeT) that progressively projects the attributes onto multiscale latent spaces. We validate our method on a set of point clouds from MVUB and MPEG and show that our method outperforms recently proposed methods and on par with the latest G-PCC version 14.
  arXiv  Detail & Related papers  (2023-03-11T23:39:30Z)
• Single-path Bit Sharing for Automatic Loss-aware Model Compression [126.98903867768732]
  Single-path Bit Sharing (SBS) is able to significantly reduce computational cost while achieving promising performance. Our SBS compressed MobileNetV2 achieves 22.6x Bit-Operation (BOP) reduction with only 0.1% drop in the Top-1 accuracy.
  arXiv  Detail & Related papers  (2021-01-13T08:28:21Z)
• OctSqueeze: Octree-Structured Entropy Model for LiDAR Compression [77.8842824702423]
  We present a novel deep compression algorithm to reduce the memory footprint of LiDAR point clouds. Our method exploits the sparsity and structural redundancy between points to reduce the memory footprint. Our algorithm can be used to reduce the onboard and offboard storage of LiDAR points for applications such as self-driving cars.
  arXiv  Detail & Related papers  (2020-05-14T17:48:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.