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Related Experiment Video

Updated: Jul 24, 2025

High-speed Particle Image Velocimetry Near Surfaces
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Variable Rate Point Cloud Geometry Compression Method.

Lehui Zhuang1, Jin Tian1, Yujin Zhang1

  • 1The School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China.

Sensors (Basel, Switzerland)
|July 8, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel variable rate point cloud compression method. It enables flexible compression rates within a single model, significantly reducing training time and storage needs.

Keywords:
contrastive learningpoint cloud compressionvariable bit rate

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Area of Science:

  • Computer Vision
  • Signal Processing
  • Data Compression

Background:

  • 3D point clouds are crucial in industrial applications, driving the need for efficient compression techniques.
  • Existing learned point cloud compression methods require training numerous models for different compression rates, leading to high training costs and storage demands.
  • A one-to-one mapping between models and compression rates limits flexibility and efficiency.

Purpose of the Study:

  • To develop a single, variable rate point cloud compression model adaptable to different compression needs.
  • To overcome the limited rate range issue in traditional variable rate models.
  • To enhance the visual quality of reconstructed point clouds.

Main Methods:

  • A variable rate point cloud compression method adjusting compression by a hyperparameter within a single model.
  • A rate expansion technique utilizing contrastive learning to broaden the model's bit rate range.
  • Boundary learning to improve the classification of boundary points and overall model performance.

Main Results:

  • The proposed method successfully achieves variable rate compression across a wide bit rate range.
  • It demonstrates superior performance compared to G-PCC, with over 70% BD-Rate reduction.
  • The method achieves performance comparable to existing learned methods at high bit rates.

Conclusions:

  • The developed method offers a flexible and efficient solution for point cloud compression.
  • It significantly reduces computational overhead and storage requirements.
  • The approach enhances reconstructed point cloud quality and broadens applicability in industrial scenarios.