Photo tourism: exploring photo collections in 3D
ACM SIGGRAPH 2006 Papers
Fast incremental square root information smoothing
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Vast-scale Outdoor Navigation Using Adaptive Relative Bundle Adjustment
International Journal of Robotics Research
Bundle adjustment in the large
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Scene reconstruction and visualization from internet photo collections
Scene reconstruction and visualization from internet photo collections
Relative bundle adjustment based on trifocal constraints
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part II
Fully Automatic Registration of Image Sets on Approximate Geometry
International Journal of Computer Vision
Large-Scale bundle adjustment by parameter vector partition
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part IV
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We propose a spectral partitioning approach for large-scaleoptimization problems, specifically structure from motion.In structure from motion, partitioning methods reduce theproblem into smaller and better conditioned subproblemswhich can be efficiently optimized.Our partitioning methoduses only the Hessian of the reprojection error and its eigenvector.We show that partitioned systems that preserve theeigenvectors corresponding to small eigenvalues result inlower residual error when optimized.We create partitionsby clustering the entries of the eigenvectors of the Hessiancorresponding to small eigenvalues.This is a more generaltechnique than relying on domain knowledge and heuristicssuch as bottom-up structure from motion approaches.Simultaneously,it takes advantage of more information thangeneric matrix partitioning algorithms.