arxiv_cv 90% Match Research Paper Computer Vision Researchers,3D Graphics Engineers,VR/AR Developers,Machine Learning Engineers,Data Compression Specialists 1 week ago

AnyPcc: Compressing Any Point Cloud with a Single Universal Model

computer-vision › 3d-vision

📄 Abstract

Abstract: Generalization remains a critical challenge for deep learning-based point cloud geometry compression. We argue this stems from two key limitations: the lack of robust context models and the inefficient handling of out-of-distribution (OOD) data. To address both, we introduce AnyPcc, a universal point cloud compression framework. AnyPcc first employs a Universal Context Model that leverages priors from both spatial and channel-wise grouping to capture robust contextual dependencies. Second, our novel Instance-Adaptive Fine-Tuning (IAFT) strategy tackles OOD data by synergizing explicit and implicit compression paradigms. It fine-tunes a small subset of network weights for each instance and incorporates them into the bitstream, where the marginal bit cost of the weights is dwarfed by the resulting savings in geometry compression. Extensive experiments on a benchmark of 15 diverse datasets confirm that AnyPcc sets a new state-of-the-art in point cloud compression. Our code and datasets will be released to encourage reproducible research.

Authors (5)

Kangli Wang

Qianxi Yi

Yuqi Ye

Shihao Li

Wei Gao

Submitted

October 23, 2025

arXiv Category

cs.CV

arXiv PDF Code

Key Contributions

Introduces AnyPcc, a universal point cloud compression framework that addresses generalization issues. It features a Universal Context Model using spatial/channel grouping and an Instance-Adaptive Fine-Tuning (IAFT) strategy to handle OOD data by fine-tuning a subset of weights per instance.

Business Value

Enables more efficient storage and transmission of large 3D datasets, crucial for VR/AR, autonomous systems, and 3D content creation, reducing bandwidth and storage costs.

Paper Metadata

Innovation Type

Algorithmic and Architectural Innovation

Deployment Feasibility

High. The goal is a single universal model, simplifying deployment. IAFT adds a small per-instance overhead but improves robustness.

Limitations Addressed

Addresses the critical challenge of generalization in deep learning-based point cloud compression, particularly the handling of out-of-distribution data and the need for robust context models.

Performance Gains

Sets new state-of-the-art performance on a benchmark of 15 diverse datasets.

View Code on GitHub

Technical Tags

Point Cloud CompressionDeep LearningGeneralizationOut-of-Distribution (OOD) DataUniversal ModelContext ModelsInstance-Adaptive Fine-Tuning (IAFT)Spatial GroupingChannel-wise GroupingBenchmark Datasets

Research Topics

Computer Vision3D Data ProcessingMachine LearningDeep LearningData CompressionPoint Clouds

Methods & Architectures

Universal Context ModelInstance-Adaptive Fine-Tuning (IAFT)Spatial and channel-wise groupingExplicit and implicit compression paradigms Deep Neural Networks (DNNs)

Applications & Tasks

3D Graphics Virtual Reality (VR) Augmented Reality (AR) Autonomous Driving Robotics 3D Scanning Generalization challenge in point cloud compressionHandling out-of-distribution dataLack of robust context modelsInefficient compression for diverse point clouds Compressing any point cloud with a single modelImproving compression performance on diverse datasetsAdapting compression to individual point cloud instancesCapturing robust contextual dependencies

Datasets & Benchmarks

Datasets

15 diverse datasets (benchmark)

Benchmarks

New state-of-the-art in point cloud compression

Related Fields

3D Computer VisionGeometric Deep LearningData CompressionPoint Cloud ProcessingVirtual Reality

Keywords

point cloud compression3D visiondeep learninggeneralizationout-of-distributionuniversal modelinstance-adaptive fine-tuningIAFT3D dataVRARautonomous driving

Academic Context

#Computer Vision#3D Data Processing#Machine Learning#Deep Learning#Data Compression#Point Clouds

Commercial Potential

Potential Products

Efficient point cloud compression libraries/SDKsCloud services for 3D data processing and streamingTools for optimizing 3D asset pipelines

Target Industries

GamingEntertainmentAutomotiveRoboticsArchitectureManufacturing

Use Case Examples

Streaming high-fidelity 3D environments for VR experiencesReducing the size of LiDAR data for autonomous vehiclesEfficiently storing and sharing 3D scans of objects or environments

Competitive Edge

Aims to be a universal solution, outperforming specialized models across a wide range of point cloud datasets due to its robust context modeling and adaptive fine-tuning.

Market Opportunity

Large and growing market for 3D data processing and compression, driven by VR/AR and autonomous systems.

Revenue Models

Licensing of the compression technologyoffering cloud-based compression servicesor integrating into 3D software suites.

Resource Requirements

Compute Needs

Training requires significant GPU resources. Inference requirements depend on the model size and IAFT overhead.

Data Requirements

Requires diverse point cloud datasets covering various densities, structures, and noise levels to train and evaluate the universal model.

Deployment Constraints

The IAFT step adds a small computational cost per instance during compression/decompression. Ensuring the universal model generalizes to completely unseen point cloud types.

Scalability

The universal model approach is inherently scalable, aiming to handle any point cloud. IAFT adds per-instance processing.

Production Readiness

Maturity Level

Research

Time to Market

1-2 years

Licensing

Likely open-source (code provided).

Patent Potential

High, for the AnyPcc framework, Universal Context Model, and IAFT strategy.

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