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View planning algorithms for fully automatic 3D acquisition of unknown objects

Abstract : This paper addresses the view planning problem for the digitization of 3D objects without prior knowledge on their shape and presents a novel method called Orientation, Angle and Covering (OAC). The proposed method is based on a combination of two concepts: the Mass Vector Chains (MVC) and the Measurability Matrix. The MVC allows to define the global orientation of the scanned part. All of the view points are sorted using an orientation criterion to define a first set of candidates for the Next Best View (NBV). The Measurability Matrix allows to determine the coverage rate for each candidate. The covering criterion leads to reduce the number of view points of the first set. A clustering step is finally performed, and the NBV is chosen as one of the most representative modes. Experiments demonstrate the feasibility and the efficiency of the approach.
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Contributor : Souhaiel Khalfaoui <>
Submitted on : Tuesday, March 12, 2013 - 11:06:31 AM
Last modification on : Friday, July 17, 2020 - 2:54:05 PM


  • HAL Id : hal-00799388, version 1


Souhaiel Khalfaoui, Ralph Seulin, Yohan Fougerolle, David Fofi. View planning algorithms for fully automatic 3D acquisition of unknown objects. 11th International Conference on Quality Control by Artificial Vision, May 2013, Fukuoka, Japan. ⟨hal-00799388⟩



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