Primitive shape fitting in point clouds using the bees algorithm

Research output: Contribution to journalArticle

Authors

Colleges, School and Institutes

External organisations

  • University of Surrey
  • University of Lincoln
  • University of Birmingham

Abstract

In this study the problem of fitting shape primitives to point cloud scenes was tackled as a parameter optimisation procedure, and solved using the popular Bees Algorithm. Tested on three sets of clean and differently blurred point cloud models, the Bees Algorithm obtained performances comparable to those obtained using the state-of-the-art RANSAC method, and superior to those obtained by an evolutionary algorithm. Shape fitting times were compatible with real-time application. The main advantage of the Bees Algorithm over standard methods is that it doesn't rely on ad hoc assumptions about the nature of the point cloud model like RANSAC approximation tolerance.

Details

Original languageEnglish
Article number5198
JournalApplied Sciences (Switzerland)
Volume9
Issue number23
Early online date29 Nov 2019
Publication statusE-pub ahead of print - 29 Nov 2019

Keywords

  • machine vision, optimisation, primitive fitting, bees algorithm