Using Curvilinear Features in Focus for Registering a Single Image to a 3D Object

In the context of 2D/3D registration, this paper introduces an approach that allows for matching features detected in two different modalities, photographs, and 3D models, by using a common 2D representation. More precisely, 2D images are matched with a set of depth images representing the 3D model....

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Bibliographic Details
Published in:IEEE transactions on image processing 2019-09, Vol.28 (9), p.4429-4443
Main Authors: Rashwan, Hatem A., Chambon, Sylvie, Gurdjos, Pierre, Morin, Geraldine, Charvillat, Vincent
Format: Article
Language:English
Subjects:
2D-3D registration
Adaptation models
Algorithms
Computer Science
Curvature
curvilinear saliency
Detectors
Feature extraction
Histograms
Image Processing
Lighting
object detection
pose estimation
Registration
Salience
Solid modeling
Three dimensional models
Three-dimensional displays
Two dimensional displays
Two dimensional models
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Summary:In the context of 2D/3D registration, this paper introduces an approach that allows for matching features detected in two different modalities, photographs, and 3D models, by using a common 2D representation. More precisely, 2D images are matched with a set of depth images representing the 3D model. After introducing the concept of Curvilinear Saliency, which is related to curvature estimation, we propose a new ridge and valley detector for depth images rendered from 3D models. A variant of this detector is adapted to photographs, first by considering multi-scale features and second by integrating the focus curve principle. Finally, a registration algorithm determines the correct view of the 3D model and, thus, the pose of the photograph. This approach relies on the Histogram of Curvilinear Saliency (HCS), an adaptation of the Histogram of Oriented Gradients (HOG) to the proposed features in 2D and 3D. The presented results highlight both the quality of the features detected in terms of repeatability and the interest of the approach for registration and pose estimation.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2019.2911484