Practical structure and motion recovery from two uncalibrated images using εε Constrained Adaptive Differential Evolution

Metric 3D reconstruction from two uncalibrated images involves estimating both the camera parameters and the 3D structure of a scene, which is known to be sensitive to the quality of image correspondences. In this paper, the above problem is recast as a single constrained optimization problem, which can be efficiently solved by a new modified εε Constrained Adaptive Differential Evolution (εADE)(εADE) optimizer, within which noticeable acceleration of convergence rate has been achieved by incorporating geometrically meaningful evolutionary operations. The proposed approach avoids solving the inverse 3D reconstruction problem by directly searching for the globally optimal 3D structure while satisfying the epipolar geometry and the cheirality constraints. Given a set of outlier affected noisy image correspondences, the camera calibration and scene structure can be simultaneously estimated in our global optimization framework. Extensive experimental validation on both synthetic data and real images were carried out. The performance of our proposed method is compared with that of four well-known fundamental matrix estimation methods, each of which is combined with analytical focal length estimation, optimal triangulation and bundle adjustment optimization. Statistical analysis of the results demonstrates that our method can significantly improve the accuracy of camera calibration and scene reconstruction. The stable, accurate numerical performance as well as fast convergence rate make our method a practical one.

► A single constrained optimization formulation of two-view metric 3D reconstruction.
► Direct structure and motion search to avoid solving inverse 3D reconstruction.
► Geometrically meaningful evolutionary operations to achieve faster convergence.
► Improved calibration and reconstruction accuracy verified by statistical analysis.