Multi-view Matching for Unordered Image Sets, or "How Do I Organize My Holiday Snaps?"
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Surviving Dominant Planes in Uncalibrated Structure and Motion Recovery
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Bundle Adjustment - A Modern Synthesis
ICCV '99 Proceedings of the International Workshop on Vision Algorithms: Theory and Practice
An Assessment of Information Criteria for Motion Model Selection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Visual Modeling with a Hand-Held Camera
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stereo Processing by Semiglobal Matching and Mutual Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detailed Real-Time Urban 3D Reconstruction from Video
International Journal of Computer Vision
Evaluation of Stereo Matching Costs on Images with Radiometric Differences
IEEE Transactions on Pattern Analysis and Machine Intelligence
Building Rome on a cloudless day
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Generalized subgraph preconditioners for large-scale bundle adjustment
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
RECON: Scale-adaptive robust estimation via Residual Consensus
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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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).