Sorting unorganized photo sets for urban reconstruction
Graphical Models
An optimization approach for extracting and encoding consistent maps in a shape collection
ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH Asia 2012
Visibility probability structure from sfm datasets and applications
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Epipolar geometry estimation for urban scenes with repetitive structures
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part IV
Coupled structure-from-motion and 3D symmetry detection for urban facades
ACM Transactions on Graphics (TOG)
Bayesian perspective for the registration of multiple 3D views
Computer Vision and Image Understanding
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Most existing structure from motion (SFM) approaches for unordered images cannot handle multiple instances of the same structure in the scene. When image pairs containing different instances are matched based on visual similarity, the pairwise geometric relations as well as the correspondences inferred from such pairs are erroneous, which can lead to catastrophic failures in the reconstruction. In this paper, we investigate the geometric ambiguities caused by the presence of repeated or duplicate structures and show that to disambiguate between multiple hypotheses requires more than pure geometric reasoning. We couple an expectation maximization (EM)-based algorithm that estimates camera poses and identifies the false match-pairs with an efficient sampling method to discover plausible data association hypotheses. The sampling method is informed by geometric and image-based cues. Our algorithm usually recovers the correct data association, even in the presence of large numbers of false pairwise matches.