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Improved Video-Based Point Cloud Compression via Segmentation.

Faranak Tohidi1, Manoranjan Paul1, Anwaar Ulhaq2

  • 1School of Computing Mathematics and Engineering, Charles Sturt University, Bathurst, NSW 2795, Australia.

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Summary
This summary is machine-generated.

This study introduces a new method for compressing dynamic 3D point clouds, improving data efficiency and visual quality. The approach enhances point cloud compression by focusing on shape similarity and occlusion for better patch generation.

Keywords:
3D videoV-PCCcompressiondynamic point cloudsegmentation

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Area of Science:

  • Computer Vision
  • 3D Data Processing
  • Data Compression

Background:

  • Point clouds are essential for 3D representations in fields like virtual and augmented reality.
  • Compressing large dynamic point cloud data is crucial for efficient transmission and storage.
  • Current video-based point cloud compression (V-PCC) methods involve computationally intensive steps and can lead to information loss and artifacts.

Purpose of the Study:

  • To develop an improved method for dynamic point cloud compression.
  • To reduce data volume while preserving geometric and texture information.
  • To minimize artifacts and enhance user perception compared to existing standards.

Main Methods:

  • A novel segmentation strategy for dynamic point clouds based on shape similarity and occlusion.
  • Generating patches after segmentation to better preserve point proximity and original data.
  • Utilizing density and occlusion properties for enhanced segmentation before patch generation.

Main Results:

  • The proposed method significantly outperforms the V-PCC standard in rate-distortion performance.
  • Experimental results show superior subjective quality testing for both geometric and texture data.
  • The new approach retains more original points and proximity information compared to V-PCC.

Conclusions:

  • The proposed segmentation-based approach offers a more effective way to compress dynamic point clouds.
  • This method addresses limitations of current V-PCC standards by preserving data integrity.
  • The findings suggest a significant advancement in 3D point cloud compression technology.