Automatic symmetry plane estimation of bilateral objects in point clouds

Benoît Combès 1 Robin Hennessy 2 John Waddington 2 Neil Roberts 3 Sylvain Prima 1
1 VisAGeS - Vision, Action et Gestion d'informations en Santé
INSERM - Institut National de la Santé et de la Recherche Médicale : U746, Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : In this paper, the problem of estimating automatically the symmetry plane of bilateral objects (having perfect or imperfect mirror symmetry) in point clouds is reexamined. Classical methods, mostly based on the ICP algorithm, are shown to be limited and complicated by an inappropriate parameterization of the problem. First, we show how an adequate parameterization, used in an ICP-like scheme, can lead to a simpler, more accurate and faster algorithm. Then, using this parameterization, we reinterpret the problem in a probabilistic framework, and use the maximum likelihood principle to define the optimal symmetry plane. This problem can be solved efficiently using an EM algorithm. The resulting iterative scheme can be seen as an ICP-like algorithm with multiple matches between the two sides of the object. This new algorithm, implemented using a multiscale, multiresolution approach, is evaluated in terms of accuracy, robustness and speed on ground truth data, and some results on real data are presented.
Document type :
Conference papers
Complete list of metadatas

Cited literature [24 references]  Display  Hide  Download

https://hal.inria.fr/inria-00331758
Contributor : Benoît Combes <>
Submitted on : Friday, October 17, 2008 - 3:40:18 PM
Last modification on : Tuesday, March 26, 2019 - 3:54:24 PM
Long-term archiving on : Tuesday, October 9, 2012 - 1:58:23 PM

File

cvpr2008CombesFinal.pdf
Files produced by the author(s)

Identifiers

Citation

Benoît Combès, Robin Hennessy, John Waddington, Neil Roberts, Sylvain Prima. Automatic symmetry plane estimation of bilateral objects in point clouds. IEEE Conference on Computer Vision and Pattern Recognition (CVPR'2008), Jun 2008, Anchorage, United States. ⟨10.1109/CVPR.2008.4587605⟩. ⟨inria-00331758⟩

Share

Metrics

Record views

526

Files downloads

1026