Gradient Response Maps for Real-Time Detection of Textureless Objects

We present a method for real-time 3D object instance detection that does not require a time-consuming training stage, and can handle untextured objects. At its core, our approach is a novel image representation for template matching designed to be robust to small image transformations. This robustne...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence Vol. 34; no. 5; pp. 876 - 888
Main Authors: Hinterstoisser, S., Cagniart, C., Ilic, S., Sturm, P., Navab, N., Fua, P., Lepetit, V.
Format: Journal Article
Language:English
Published: Los Alamitos, CA IEEE 01.05.2012
IEEE Computer Society
Subjects:
Applied sciences
Artificial intelligence
Clutter
Computer science; control theory; systems
Computer vision
Exact sciences and technology
Humans
Image edge detection
Image Processing, Computer-Assisted - methods
Imaging, Three-Dimensional
multimodality template matching
Pattern recognition. Digital image processing. Computational geometry
Real time systems
real-time detection and object recognition
Reproducibility of Results
Robustness
Surface Properties
Three dimensional displays
tracking
Training
Transforms
Computer vision
Tracking
Measurement sensor
Syntactic analysis
Cluttered environment
Real time
Object recognition
Surface normal
Image analysis
Response time
Image matching
Multimodality
Computer architecture
Object detection
Robustness
Localization
multimodality template matching
real-time detection and object recognition
Pattern matching
ISSN:0162-8828, 1939-3539, 2160-9292, 1939-3539
Online Access:Get full text
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Description
Summary:We present a method for real-time 3D object instance detection that does not require a time-consuming training stage, and can handle untextured objects. At its core, our approach is a novel image representation for template matching designed to be robust to small image transformations. This robustness is based on spread image gradient orientations and allows us to test only a small subset of all possible pixel locations when parsing the image, and to represent a 3D object with a limited set of templates. In addition, we demonstrate that if a dense depth sensor is available we can extend our approach for an even better performance also taking 3D surface normal orientations into account. We show how to take advantage of the architecture of modern computers to build an efficient but very discriminant representation of the input images that can be used to consider thousands of templates in real time. We demonstrate in many experiments on real data that our method is much faster and more robust with respect to background clutter than current state-of-the-art methods.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:0162-8828
1939-3539
2160-9292
1939-3539
DOI:10.1109/TPAMI.2011.206