DiffCom: Decoupled Sparse Priors Guided Diffusion Compression for Point Clouds

• URL: http://arxiv.org/abs/2411.13860v3
• Date: Tue, 07 Oct 2025 01:53:16 GMT
• Title: DiffCom: Decoupled Sparse Priors Guided Diffusion Compression for Point Clouds
• Authors: Xiaoge Zhang, Zijie Wu, Mehwish Nasim, Mingtao Feng, Saeed Anwar, Ajmal Mian,
• Abstract summary: Lossy compression relies on an autoencoder to transform a point cloud into latent points for storage.<n>We propose a diffusion-based framework guided by sparse priors that achieves high reconstruction quality, especially at lows.
• Score: 54.96190721255167
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Lossy compression relies on an autoencoder to transform a point cloud into latent points for storage, leaving the inherent redundancy of latent representations unexplored. To reduce redundancy in latent points, we propose a diffusion-based framework guided by sparse priors that achieves high reconstruction quality, especially at low bitrates. Our approach features an efficient dual-density data flow that relaxes size constraints on latent points. It hybridizes a probabilistic conditional diffusion model to encapsulate essential details for reconstruction within sparse priors, which are decoupled hierarchically into intra- and inter-point priors. Specifically, our DiffCom encodes the original point cloud into latent points and decoupled sparse priors through separate encoders. To dynamically attend to geometric and semantic cues from the priors at each encoding and decoding layer, we employ an attention-guided latent denoiser conditioned on the decoupled priors. Additionally, we integrate the local distribution into the arithmetic encoder and decoder to enhance local context modeling of the sparse points. The original point cloud is reconstructed through a point decoder. Compared to state-of-the-art methods, our approach achieves a superior rate-distortion trade-off, as evidenced by extensive evaluations on the ShapeNet dataset and standard test datasets from the MPEG PCC Group.

Related papers

• Have We Scene It All? Scene Graph-Aware Deep Point Cloud Compression [18.40946383877556]
  We propose a deep compression framework based on semantic scene graphs.<n>We show that the framework achieves state-of-the-art compression rates, reducing data size by up to 98%.<n>It supports downstream applications such as multi-robot pose graph optimization and map merging.
  arXiv  Detail & Related papers  (2025-10-09T17:45:09Z)
• Hierarchical Attention Networks for Lossless Point Cloud Attribute Compression [22.234604407822673]
  We propose a deep hierarchical attention context model for attribute compression of point clouds. A simple and effective Level of Detail (LoD) structure is introduced to yield a coarse-to-fine representation. Points within the same refinement level are encoded in parallel, sharing a common context point group.
  arXiv  Detail & Related papers  (2025-04-01T07:14:10Z)
• RBFIM: Perceptual Quality Assessment for Compressed Point Clouds Using Radial Basis Function Interpolation [58.04300937361664]
  One of the main challenges in point cloud compression (PCC) is how to evaluate the perceived distortion so that the RB can be optimized for perceptual quality. We propose a novel assessment method, utilizing radial basis function (RBF) to convert discrete point features into a continuous feature function for the distorted point cloud.
  arXiv  Detail & Related papers  (2025-03-18T11:25:55Z)
• 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)
• Fast Point Cloud Geometry Compression with Context-based Residual Coding and INR-based Refinement [19.575833741231953]
  We use the KNN method to determine the neighborhoods of raw surface points. A conditional probability model is adaptive to local geometry, leading to significant rate reduction. We incorporate an implicit neural representation into the refinement layer, allowing the decoder to sample points on the underlying surface at arbitrary densities.
  arXiv  Detail & Related papers  (2024-08-06T05:24:06Z)
• Hierarchical Prior-based Super Resolution for Point Cloud Geometry Compression [39.052583172727324]
  The Geometry-based Point Cloud Compression (G-PCC) has been developed by the Moving Picture Experts Group to compress point clouds. This paper proposes a hierarchical prior-based super resolution method for point cloud geometry compression.
  arXiv  Detail & Related papers  (2024-02-17T11:15:38Z)
• Joint Learning for Scattered Point Cloud Understanding with Hierarchical Self-Distillation [34.26170741722835]
  We propose an end-to-end architecture that compensates for and identifies partial point clouds on the fly. hierarchical self-distillation (HSD) can be applied to arbitrary hierarchy-based point cloud methods.
  arXiv  Detail & Related papers  (2023-12-28T08:51:04Z)
• Point Cloud Pre-training with Diffusion Models [62.12279263217138]
  We propose a novel pre-training method called Point cloud Diffusion pre-training (PointDif) PointDif achieves substantial improvement across various real-world datasets for diverse downstream tasks such as classification, segmentation and detection.
  arXiv  Detail & Related papers  (2023-11-25T08:10:05Z)
• Controllable Mesh Generation Through Sparse Latent Point Diffusion Models [105.83595545314334]
  We design a novel sparse latent point diffusion model for mesh generation. Our key insight is to regard point clouds as an intermediate representation of meshes, and model the distribution of point clouds instead. Our proposed sparse latent point diffusion model achieves superior performance in terms of generation quality and controllability.
  arXiv  Detail & Related papers  (2023-03-14T14:25:29Z)
• SoftPool++: An Encoder-Decoder Network for Point Cloud Completion [93.54286830844134]
  We propose a novel convolutional operator for the task of point cloud completion. The proposed operator does not require any max-pooling or voxelization operation. We show that our approach achieves state-of-the-art performance in shape completion at low and high resolutions.
  arXiv  Detail & Related papers  (2022-05-08T15:31:36Z)
• Point Cloud Compression with Sibling Context and Surface Priors [47.96018990521301]
  We present a novel octree-based multi-level framework for large-scale point cloud compression. In this framework, we propose a new entropy model that explores the hierarchical dependency in an octree. We locally fit surfaces with a voxel-based geometry-aware module to provide geometric priors in entropy encoding.
  arXiv  Detail & Related papers  (2022-05-02T09:13:26Z)
• Density-preserving Deep Point Cloud Compression [72.0703956923403]
  We propose a novel deep point cloud compression method that preserves local density information. Our method works in an auto-encoder fashion: the encoder downsamples the points and learns point-wise features, while the decoder upsamples the points using these features.
  arXiv  Detail & Related papers  (2022-04-27T03:42:15Z)
• Upsampling Autoencoder for Self-Supervised Point Cloud Learning [11.19408173558718]
  We propose a self-supervised pretraining model for point cloud learning without human annotations. Upsampling operation encourages the network to capture both high-level semantic information and low-level geometric information of the point cloud. We find that our UAE outperforms previous state-of-the-art methods in shape classification, part segmentation and point cloud upsampling tasks.
  arXiv  Detail & Related papers  (2022-03-21T07:20:37Z)
• Point Set Self-Embedding [63.23565826873297]
  This work presents an innovative method for point set self-embedding, that encodes structural information of a dense point set into its sparser version in a visual but imperceptible form. The self-embedded point set can function as the ordinary downsampled one and be visualized efficiently on mobile devices. We can leverage the self-embedded information to fully restore the original point set for detailed analysis on remote servers.
  arXiv  Detail & Related papers  (2022-02-28T07:03:33Z)
• Reducing Redundancy in the Bottleneck Representation of the Autoencoders [98.78384185493624]
  Autoencoders are a type of unsupervised neural networks, which can be used to solve various tasks. We propose a scheme to explicitly penalize feature redundancies in the bottleneck representation. We tested our approach across different tasks: dimensionality reduction using three different dataset, image compression using the MNIST dataset, and image denoising using fashion MNIST.
  arXiv  Detail & Related papers  (2022-02-09T18:48:02Z)
• SPU-Net: Self-Supervised Point Cloud Upsampling by Coarse-to-Fine Reconstruction with Self-Projection Optimization [52.20602782690776]
  It is expensive and tedious to obtain large scale paired sparse-canned point sets for training from real scanned sparse data. We propose a self-supervised point cloud upsampling network, named SPU-Net, to capture the inherent upsampling patterns of points lying on the underlying object surface. We conduct various experiments on both synthetic and real-scanned datasets, and the results demonstrate that we achieve comparable performance to the state-of-the-art supervised methods.
  arXiv  Detail & Related papers  (2020-12-08T14:14:09Z)
• Point2Mesh: A Self-Prior for Deformable Meshes [83.31236364265403]
  We introduce Point2Mesh, a technique for reconstructing a surface mesh from an input point cloud. The self-prior encapsulates reoccurring geometric repetitions from a single shape within the weights of a deep neural network. We show that Point2Mesh converges to a desirable solution; compared to a prescribed smoothness prior, which often becomes trapped in undesirable local minima.
  arXiv  Detail & Related papers  (2020-05-22T10:01:04Z)

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.