An efficient RANSAC for 3D object recognition in noisy and occluded scenes

Chavdar Papazov, Darius Burschka

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

120 Zitate (Scopus)

Abstract

In this paper, we present an efficient algorithm for 3D object recognition in presence of clutter and occlusions in noisy, sparse and unsegmented range data. The method uses a robust geometric descriptor, a hashing technique and an efficient RANSAC-like sampling strategy. We assume that each object is represented by a model consisting of a set of points with corresponding surface normals. Our method recognizes multiple model instances and estimates their position and orientation in the scene. The algorithm scales well with the number of models and its main procedure runs in linear time in the number of scene points. Moreover, the approach is conceptually simple and easy to implement. Tests on a variety of real data sets show that the proposed method performs well on noisy and cluttered scenes in which only small parts of the objects are visible.

OriginalspracheEnglisch
TitelComputer Vision, ACCV 2010 - 10th Asian Conference on Computer Vision, Revised Selected Papers
Seiten135-148
Seitenumfang14
AuflagePART 1
DOIs
PublikationsstatusVeröffentlicht - 2011
Veranstaltung10th Asian Conference on Computer Vision, ACCV 2010 - Queenstown, Neuseeland
Dauer: 8 Nov. 201012 Nov. 2010

Publikationsreihe

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NummerPART 1
Band6492 LNCS
ISSN (Print)0302-9743
ISSN (elektronisch)1611-3349

Konferenz

Konferenz10th Asian Conference on Computer Vision, ACCV 2010
Land/GebietNeuseeland
OrtQueenstown
Zeitraum8/11/1012/11/10

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