A 3D Scene Registration Method via Covariance Descriptors and an Evolutionary Stable Strategy Game Theory Solver

摘要

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.