Global iterative closet point using nested annealing for initialization

Tao Ngoc Linh, Hasegawa Hiroshi

Research output: Contribution to journalConference articlepeer-review

10 Citations (Scopus)


In computer vision, Iterative Closest Point (ICP) has been a key tool for registration algorithms, a fundamental task in computer vision. However, ICP based registration algorithms always face with local minima problem and pre-aligned pointsets are the must to guarantee correct convergence. Pre-alignment used to be carried out by our human in some mesh processing softwares. This paper provides a solution for initialization problem for registering two 3D surfaces under L2 error using ICP algorithm. Our algorithm uses a combination between Nested Annealing (NA) and ICP in which NA is used as global optimization search engine to find the global minima with a novel approach of using point based boundary searching. The algorithm uses ICP to derive local minima as well as local minima error. The integration between ICP and NA is successfully implemented and coded into a program which inputs two range image and outputs the transformation matrix between them at high accuracy and success rate.

Original languageEnglish
Pages (from-to)381-390
Number of pages10
JournalProcedia Computer Science
Issue number1
Publication statusPublished - 2015
Event19th International Conference on Knowledge Based and Intelligent Information and Engineering Systems, KES 2015 - , Singapore
Duration: 2015 Sept 72015 Sept 9


  • 3D registration
  • Global registration
  • Hybrid algorithm
  • ICP
  • Nested Annealing

ASJC Scopus subject areas

  • General Computer Science


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