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Dynamic Point Cloud Compression Based on Projections, Surface Reconstruction and Video Compression.

Emil Dumic1, Anamaria Bjelopera2, Andreas Nüchter3

  • 1Department of Electrical Engineering, University North, 104. Brigade 3, 42000 Varaždin, Croatia.

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Summary
This summary is machine-generated.

This study introduces dynamic point cloud compression using projection mapping and video codecs. Cylindrical, Miller, and Mercator projections showed superior performance in reconstructed point cloud quality.

Keywords:
3DTK toolkitPoisson surface reconstructionmap projectionsoctreepoint cloud compressionpoint-to-plane measurepoint-to-point measure

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Area of Science:

  • Computer Vision
  • Computer Graphics
  • Data Compression

Background:

  • Dynamic point clouds require efficient compression methods.
  • Existing methods may not optimally handle geometry and texture data simultaneously.
  • Advanced surface reconstruction techniques are crucial for accurate visualization.

Purpose of the Study:

  • To develop and evaluate a novel dynamic point cloud compression technique.
  • To investigate the impact of different projection types and bit depths on compression efficiency and quality.
  • To integrate surface reconstruction with video compression for enhanced point cloud representation.

Main Methods:

  • Compression using projection mapping (cylindrical, Miller, Mercator) and varying bit depths.
  • Texture map compression via Voronoi diagrams.
  • Application of specialized video codecs: FFV1 for geometry, H.265/HEVC for texture.
  • Point cloud reconstruction using Poisson surface reconstruction.
  • Quality assessment via point-to-point and point-to-plane metrics.

Main Results:

  • The proposed dynamic point cloud compression method demonstrates effectiveness.
  • Cylindrical, Miller, and Mercator projections yield improved results compared to others.
  • The combination of projection mapping, Voronoi diagrams, and specific video codecs achieves high-fidelity reconstruction.
  • Quantitative analysis confirms the performance benefits of selected projection types.

Conclusions:

  • Dynamic point cloud compression can be significantly improved using projection mapping techniques.
  • The choice of projection significantly impacts the efficiency and quality of reconstructed point clouds.
  • The integrated approach offers a promising solution for storing and transmitting dynamic 3D data.