Least-Squares Estimation of Transformation Parameters Between Two Point Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bundle Adjustment - A Modern Synthesis
ICCV '99 Proceedings of the International Workshop on Vision Algorithms: Theory and Practice
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Photo tourism: exploring photo collections in 3D
ACM SIGGRAPH 2006 Papers
Evaluation of Features Detectors and Descriptors based on 3D Objects
International Journal of Computer Vision
Modeling the World from Internet Photo Collections
International Journal of Computer Vision
Modeling and Recognition of Landmark Image Collections Using Iconic Scene Graphs
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
SBA: A software package for generic sparse bundle adjustment
ACM Transactions on Mathematical Software (TOMS)
Computer
Bundle adjustment in the large
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Efficient structure from motion by graph optimization
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Conjugate gradient bundle adjustment
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
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We present an improved bundle adjustment method based on the online learned appearance subspaces of 3D points. Our method incorporates the additional information from the learned appearance models into bundle adjustment. Through the online learning of the appearance models, we are able to include more plausible observations of 2D features across diverse viewpoints. Bundle adjustment can benefit from such an increase in the number of observations. Our formulation uses the appearance information to impose additional constraints on the optimization. The detailed experiments with ground-truth data show that the proposed method is able to enhance the reliability of 2D correspondences, and more important, can improve the accuracy of camera motion estimation and the overall quality of 3D reconstruction.