Dense 3D Reconstruction from Wide Baseline Image Sets

This paper describes an approach for Structure from Motion (SfM) for wide baselines image sets and its combination with the dense Semiglobal Matching (SGM) 3D reconstruction approach. Our approach for SfM relies on given information concerning image overlap, but can deal with large baselines and produces highly precise camera parameters and 3D points. At the core of our contribution is robust least squares adjustment with full exploitation of the covariance information from affine point matching to bundle adjustment. Reweighting for robust adjustment is based on covariance information for each individual residual. We use points detected based on Differences of Gaussians including scale and orientation information as well as a variant of the five point algorithm. A strategy similar to the Expectation Maximization (EM) algorithm is employed to extend partial solutions. The key characteristics of the approach is reliability obtained by aiming at a high precision in every step. The capabilities of our approach are demonstrated by presenting results for sets consisting of images from the ground and from small Unmanned Aircraft Systems (UASs).