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A generic scheme for progressive point cloud coding.

Yan Huang1, Jingliang Peng, C-C Jay Kuo

  • 1Department of Computer Science, University of California, Irvine, Irvine, CA 92697, USA. yanh@ics.uci.edu

IEEE Transactions on Visualization and Computer Graphics
|January 15, 2008
PubMed
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This study introduces a novel point cloud encoder for efficient 3D data compression. The method uses octree subdivision and attribute-dependent encoding, improving rate-distortion performance and offering potential for lossless compression.

Area of Science:

  • Computer Graphics
  • Data Compression
  • 3D Data Processing

Background:

  • Point cloud data represents 3D objects but poses significant compression challenges.
  • Existing methods often struggle with arbitrary topologies and diverse attributes like position, normals, and color.

Purpose of the Study:

  • To develop a unified framework for compressing various attributes of 3D point clouds.
  • To enhance rate-distortion performance and computational efficiency in point cloud encoding.

Main Methods:

  • A generic point cloud encoder utilizing iterative octree cell subdivision.
  • Approximation of point positions using geometry centers and attributes (normals, colors) via statistical averages within octree cells.
  • Attribute-dependent encoding techniques tailored to exploit data characteristics.

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Main Results:

  • Significant improvements in rate-distortion (R-D) performance compared to state-of-the-art methods.
  • Demonstrated computational advantage over existing point cloud compression techniques.
  • Potential for achieving lossless compression with optimized parameters.

Conclusions:

  • The proposed octree-based encoder offers a versatile and efficient solution for 3D point cloud compression.
  • The attribute-dependent encoding strategy effectively handles diverse data characteristics.
  • Future work can focus on lossless compression and broader applications.