Implicit Neural Compression of Point Clouds

• URL: http://arxiv.org/abs/2412.10433v2
• Date: Tue, 21 Oct 2025 00:46:08 GMT
• Title: Implicit Neural Compression of Point Clouds
• Authors: Hongning Ruan, Yulin Shao, Qianqian Yang, Liang Zhao, Zhaoyang Zhang, Dusit Niyato,
• Abstract summary: NeRC$3$ is a novel point cloud compression framework.<n>NeRC$3$ encodes both geometry and attributes of dense point clouds.<n>For dynamic point clouds, 4D-NeRC$3$ achieves superior geometry compression performance.
• Score: 61.491532656446054
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Point clouds have gained prominence across numerous applications due to their ability to accurately represent 3D objects and scenes. However, efficiently compressing unstructured, high-precision point cloud data remains a significant challenge. In this paper, we propose NeRC$^3$, a novel point cloud compression framework that leverages implicit neural representations (INRs) to encode both geometry and attributes of dense point clouds. Our approach employs two coordinate-based neural networks: one maps spatial coordinates to voxel occupancy, while the other maps occupied voxels to their attributes, thereby implicitly representing the geometry and attributes of a voxelized point cloud. The encoder quantizes and compresses network parameters alongside auxiliary information required for reconstruction, while the decoder reconstructs the original point cloud by inputting voxel coordinates into the neural networks. Furthermore, we extend our method to dynamic point cloud compression through techniques that reduce temporal redundancy, including a 4D spatio-temporal representation termed 4D-NeRC$^3$. Experimental results validate the effectiveness of our approach: For static point clouds, NeRC$^3$ outperforms octree-based G-PCC standard and existing INR-based methods. For dynamic point clouds, 4D-NeRC$^3$ achieves superior geometry compression performance compared to the latest G-PCC and V-PCC standards, while matching state-of-the-art learning-based methods. It also demonstrates competitive performance in joint geometry and attribute compression.

Related papers

• Efficient Implicit Neural Compression of Point Clouds via Learnable Activation in Latent Space [10.056460330355193]
  Implicit Neural Representations (INRs) have emerged as a powerful paradigm in deep learning. We propose textbfPICO, an INR-based framework for static point cloud compression. Our approach exhibits highly competitive results, with an average PCQM gain of $2.7 times 10-3$.
  arXiv  Detail & Related papers  (2025-04-20T03:37:32Z)
• Rendering-Oriented 3D Point Cloud Attribute Compression using Sparse Tensor-based Transformer [52.40992954884257]
  3D visualization techniques have fundamentally transformed how we interact with digital content. Massive data size of point clouds presents significant challenges in data compression. We propose an end-to-end deep learning framework that seamlessly integrates PCAC with differentiable rendering.
  arXiv  Detail & Related papers  (2024-11-12T16:12:51Z)
• Point Cloud Compression with Implicit Neural Representations: A Unified Framework [54.119415852585306]
  We present a pioneering point cloud compression framework capable of handling both geometry and attribute components. Our framework utilizes two coordinate-based neural networks to implicitly represent a voxelized point cloud. Our method exhibits high universality when contrasted with existing learning-based techniques.
  arXiv  Detail & Related papers  (2024-05-19T09:19:40Z)
• Dynamic 3D Point Cloud Sequences as 2D Videos [81.46246338686478]
  3D point cloud sequences serve as one of the most common and practical representation modalities of real-world environments. We propose a novel generic representation called textitStructured Point Cloud Videos (SPCVs) SPCVs re-organizes a point cloud sequence as a 2D video with spatial smoothness and temporal consistency, where the pixel values correspond to the 3D coordinates of points.
  arXiv  Detail & Related papers  (2024-03-02T08:18:57Z)
• PIVOT-Net: Heterogeneous Point-Voxel-Tree-based Framework for Point Cloud Compression [8.778300313732027]
  We propose a heterogeneous point cloud compression (PCC) framework. We unify typical point cloud representations -- point-based, voxel-based, and tree-based representations -- and their associated backbones. We augment the framework with a proposed context-aware upsampling for decoding and an enhanced voxel transformer for feature aggregation.
  arXiv  Detail & Related papers  (2024-02-11T16:57:08Z)
• GQE-Net: A Graph-based Quality Enhancement Network for Point Cloud Color Attribute [51.4803148196217]
  We propose a graph-based quality enhancement network (GQE-Net) to reduce color distortion in point clouds. GQE-Net uses geometry information as an auxiliary input and graph convolution blocks to extract local features efficiently. Experimental results show that our method achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2023-03-24T02:33:45Z)
• Learning Neural Volumetric Field for Point Cloud Geometry Compression [13.691147541041804]
  We propose to code the geometry of a given point cloud by learning a neural field. We divide the entire space into small cubes and represent each non-empty cube by a neural network and an input latent code. The network is shared among all the cubes in a single frame or multiple frames, to exploit the spatial and temporal redundancy.
  arXiv  Detail & Related papers  (2022-12-11T19:55:24Z)
• GRASP-Net: Geometric Residual Analysis and Synthesis for Point Cloud Compression [16.98171403698783]
  We propose a heterogeneous approach with deep learning for lossy point cloud geometry compression. Specifically, a point-based network is applied to convert the erratic local details to latent features residing on the coarse point cloud.
  arXiv  Detail & Related papers  (2022-09-09T17:09:02Z)
• 4DAC: Learning Attribute Compression for Dynamic Point Clouds [37.447460254690135]
  We study the attribute (e.g., color) compression of dynamic point clouds and present a learning-based framework, termed 4DAC. To reduce temporal redundancy within data, we first build the 3D motion estimation and motion compensation modules with deep neural networks. In addition, we also propose a deep conditional entropy model to estimate the probability distribution of the transformed coefficients.
  arXiv  Detail & Related papers  (2022-04-25T15:30:06Z)
• PointAttN: You Only Need Attention for Point Cloud Completion [89.88766317412052]
  Point cloud completion refers to completing 3D shapes from partial 3D point clouds. We propose a novel neural network for processing point cloud in a per-point manner to eliminate kNNs. The proposed framework, namely PointAttN, is simple, neat and effective, which can precisely capture the structural information of 3D shapes.
  arXiv  Detail & Related papers  (2022-03-16T09:20:01Z)
• A Conditional Point Diffusion-Refinement Paradigm for 3D Point Cloud Completion [69.32451612060214]
  Real-scanned 3D point clouds are often incomplete, and it is important to recover complete point clouds for downstream applications. Most existing point cloud completion methods use Chamfer Distance (CD) loss for training. We propose a novel Point Diffusion-Refinement (PDR) paradigm for point cloud completion.
  arXiv  Detail & Related papers  (2021-12-07T06:59:06Z)
• ParaNet: Deep Regular Representation for 3D Point Clouds [62.81379889095186]
  ParaNet is a novel end-to-end deep learning framework for representing 3D point clouds. It converts an irregular 3D point cloud into a regular 2D color image, named point geometry image (PGI) In contrast to conventional regular representation modalities based on multi-view projection and voxelization, the proposed representation is differentiable and reversible.
  arXiv  Detail & Related papers  (2020-12-05T13:19:55Z)
• Multiscale Point Cloud Geometry Compression [29.605320327889142]
  We propose a multiscale-to-end learning framework which hierarchically reconstructs the 3D Point Cloud Geometry. The framework is developed on top of a sparse convolution based autoencoder for point cloud compression and reconstruction.
  arXiv  Detail & Related papers  (2020-11-07T16:11:16Z)

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.