A Correlated Parts Model for Object Detection in Large 3D Scans

This paper addresses the problem of detecting objects in 3D scans according to object classes learned from sparse user annotation. We model objects belonging to a class by a set of fully correlated parts, encoding dependencies between local shapes of different parts as well as their relative spatial...

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Veröffentlicht in:Computer graphics forum Jg. 32; H. 2pt2; S. 205 - 214
Hauptverfasser: Sunkel, M., Jansen, S., Wand, M., Seidel, H.-P.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford, UK Blackwell Publishing Ltd 01.05.2013
Schlagworte:
3-D graphics
Algorithms
Analysis
Approximation
Artificial Intelligence [I.2.10]: Vision and Scene Understanding-Shape
Composite structures
Computer graphics
Computer Graphics [I.3.5]: Computational Geometry and Object Modeling-Object hierarchies
Correlation
Image Processing and Computer Vision [I.4.8]: Scene Analysis-Object recognition
Image processing systems
Inference
Studies
Three dimensional
Training
ISSN:0167-7055, 1467-8659
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:This paper addresses the problem of detecting objects in 3D scans according to object classes learned from sparse user annotation. We model objects belonging to a class by a set of fully correlated parts, encoding dependencies between local shapes of different parts as well as their relative spatial arrangement. For an efficient and comprehensive retrieval of instances belonging to a class of interest, we introduce a new approximate inference scheme and a corresponding planning procedure. We extend our technique to hierarchical composite structures, reducing training effort and modeling spatial relations between detected instances. We evaluate our method on a number of real‐world 3D scans and demonstrate its benefits as well as the performance of the new inference algorithm.
Bibliographie:ark:/67375/WNG-XLM99MHJ-9
istex:F607F34F3F019FD69FA6947B749B75BA85F5BCCF
ArticleID:CGF12040
SourceType-Scholarly Journals-1
ObjectType-Feature-1
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ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.12040