Large-scale 3D point cloud compression using hybrid coordinate domains

Jae Kyun Ahn, Kyu Yul Lee, Jae Young Sim, Chang-Su Kim

Research output: Contribution to journalArticlepeer-review

37 Citations (Scopus)


An adaptive range image coding algorithm for the geometry compression of large-scale 3D point clouds (LS3DPCs) is proposed in this work. A terrestrial laser scanner generates an LS3DPC by measuring the radial distances of objects in a real world scene, which can be mapped into a range image. In general, the range image exhibits different characteristics from an ordinary luminance or color image, and thus the conventional image coding techniques are not suitable for the range image coding. We propose a hybrid range image coding algorithm, which predicts the radial distance of each pixel using previously encoded neighbors adaptively in one of three coordinate domains: range image domain, height image domain, and 3D domain. We first partition an input range image into blocks of various sizes. For each block, we apply multiple prediction modes in the three domains and compute their rate-distortion costs. Then, we perform the prediction of all pixels using the optimal mode and encode the resulting prediction residuals. Experimental results show that the proposed algorithm provides significantly better compression performance on various range images than the conventional image or video coding techniques.

Original languageEnglish
Article number6955830
Pages (from-to)422-434
Number of pages13
JournalIEEE Journal on Selected Topics in Signal Processing
Issue number3
Publication statusPublished - 2015 Apr 1

Bibliographical note

Publisher Copyright:
© 2014 IEEE.


  • Cloud compression
  • Large-scale 3D point clouds (LS3DPC)
  • Point
  • Radial distance prediction
  • Range image compression
  • Terrestrial laser scanner

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


Dive into the research topics of 'Large-scale 3D point cloud compression using hybrid coordinate domains'. Together they form a unique fingerprint.

Cite this