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This chapter methodically examines static point cloud geometry compression techniques, encompassing both lossless and lossy approaches. It further organizes these approaches into three primary frameworks, including point-based compression, voxel-based compression, and octree-based compression. By analyzing the core mechanisms and distinguishing features of prominent algorithms in each category, the discussion evaluates their operational contexts and compression effectiveness. This in-depth investigation establishes foundational insights and methodological references to advance the development of static point cloud geometry coding technology.

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Deep Learning–Based Static 3D Point Cloud Geometry Coding

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  • 发表日期：

    2026年

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  • Wei Gao

摘要

This chapter methodically examines static point cloud geometry compression techniques, encompassing both lossless and lossy approaches. It further organizes these approaches into three primary frameworks, including point-based compression, voxel-based compression, and octree-based compression. By analyzing the core mechanisms and distinguishing features of prominent algorithms in each category, the discussion evaluates their operational contexts and compression effectiveness. This in-depth investigation establishes foundational insights and methodological references to advance the development of static point cloud geometry coding technology.