A 3D Scene Registration Method via Covariance Descriptors and an Evolutionary Stable Strategy Game Theory Solver: Fusing Photometric and Shape-Based Features

In this paper we provide an integrated approach for matching patterns in scenes combining 3D and visual information. For local definition of points we propose a descriptor based on the notion of covariance of features for fusion of shape and color information of 3D surfaces, so-called multi-scale co...

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Bibliographic Details
Published in:International journal of computer vision 2015-12, Vol.115 (3), p.306-329
Main Authors: Cirujeda, Pol, Dicente Cid, Yashin, Mateo, Xavier, Binefa, Xavier
Format: Article
Language:English
Subjects:
3-D graphics
African American studies
Algorithms
Analysis
Artificial Intelligence
Color
Computer Imaging
Computer Science
Computer vision
Covariance
Evolutionary
Game theory
Image Processing and Computer Vision
Image processing systems
Matching
Methods
Noise
Pattern Recognition
Pattern Recognition and Graphics
Registration
State of the art
Three dimensional
Transformations
Vision
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Summary:In this paper we provide an integrated approach for matching patterns in scenes combining 3D and visual information. For local definition of points we propose a descriptor based on the notion of covariance of features for fusion of shape and color information of 3D surfaces, so-called multi-scale covariance descriptor (MCOV). The intrinsic properties of this descriptor are many: it is invariant to spatial rigid transformations, and robust to noise and resolution changes; it can also be used for characteristic point detection; and lies on top of a manifold topology which allows the use of analytical metric properties. This descriptor is complemented with a game theoretic approach for solving the matching correspondences under global geometric constraints. This layer offers a comprehensive understanding of the scene and avoids possible mismatches due to repeated areas or symmetries—which would be impossibly identified by the detector solely at a local level. Our solution is able to accurately match different views of a scene even under spatial transformations, high noise levels and with small overlap between views, outperforming state-of-the-art approaches. Results are validated by comparing MCOV against other state-of-the-art 3D point descriptor methods, and matching complex 3D and color scenes under several challenging conditions.
ISSN:0920-5691
1573-1405
DOI:10.1007/s11263-015-0820-2