Object Recognition Using Constraints from Primitive Shape Matching

In this paper, an object recognition and pose estimation approach based on constraints from primitive shape matching is presented. Additionally, an approach for primitive shape detection from point clouds using an energy minimization formulation is presented. Each primitive shape in an object adds g...

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Published inAdvances in Visual Computing pp. 783 - 792
Main Authors Somani, Nikhil, Cai, Caixia, Perzylo, Alexander, Rickert, Markus, Knoll, Alois
Format Book Chapter
LanguageEnglish
Published Cham Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
Feature Vector
Label Cost
Label Energy
Object Recognition
Point Cloud
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Abstract In this paper, an object recognition and pose estimation approach based on constraints from primitive shape matching is presented. Additionally, an approach for primitive shape detection from point clouds using an energy minimization formulation is presented. Each primitive shape in an object adds geometric constraints on the object’s pose. An algorithm is proposed to find minimal sets of primitive shapes which are sufficient to determine the complete 3D position and orientation of a rigid object. The pose is estimated using a linear least squares solver over the combination of constraints enforced by the primitive shapes. Experiments illustrating the primitive shape decomposition of object models, detection of these minimal sets, feature vector calculation for sets of shapes and object pose estimation have been presented on simulated and real data.
AbstractList In this paper, an object recognition and pose estimation approach based on constraints from primitive shape matching is presented. Additionally, an approach for primitive shape detection from point clouds using an energy minimization formulation is presented. Each primitive shape in an object adds geometric constraints on the object’s pose. An algorithm is proposed to find minimal sets of primitive shapes which are sufficient to determine the complete 3D position and orientation of a rigid object. The pose is estimated using a linear least squares solver over the combination of constraints enforced by the primitive shapes. Experiments illustrating the primitive shape decomposition of object models, detection of these minimal sets, feature vector calculation for sets of shapes and object pose estimation have been presented on simulated and real data.
Author Perzylo, Alexander
Somani, Nikhil
Knoll, Alois
Cai, Caixia
Rickert, Markus
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DOI 10.1007/978-3-319-14249-4_75
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Zhang, Hui
Bebis, George
Carlson, Mark
Drucker, Steven M.
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Notes The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement n 287787 in the project SMErobotics, the European Robotics Initiative for Strengthening the Competitiveness of SMEs in Manufacturing by integrating aspects of cognitive systems.
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PublicationSeriesTitle Lecture Notes in Computer Science
PublicationSubtitle 10th International Symposium, ISVC 2014, Las Vegas, NV, USA, December 8-10, 2014, Proceedings, Part I
PublicationTitle Advances in Visual Computing
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Snippet In this paper, an object recognition and pose estimation approach based on constraints from primitive shape matching is presented. Additionally, an approach...
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SourceType Publisher
StartPage 783
SubjectTerms Feature Vector
Label Cost
Label Energy
Object Recognition
Point Cloud
Title Object Recognition Using Constraints from Primitive Shape Matching
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