Motion and Structure From Two Perspective Views: Algorithms, Error Analysis, and Error Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Inherent Ambiguities in Recovering 3-D Motion and Structure from a Noisy Flow Field
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Analysis of Inherent Ambiguities in Recovering 3-D Motion from a Noisy Flow Field
IEEE Transactions on Pattern Analysis and Machine Intelligence
Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
Performance of optical flow techniques
International Journal of Computer Vision
Linear subspace methods for recovering translational direction
Proceedings of the 1991 York conference on Spacial vision in humans and robots
Shape Ambiguities in Structure From Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Optimization for Geometric Computation: Theory and Practice
Statistical Optimization for Geometric Computation: Theory and Practice
Optimal Motion and Structure Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparison of Approaches to Egomotion Computation
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Representation of Scenes from Collections of Images
VSR '95 Proceedings of the IEEE Workshop on Representation of Visual Scenes
Characterizing Depth Distortion under Different Generic Motions
International Journal of Computer Vision
Optimization Criteria and Geometric Algorithms for Motion and Structure Estimation
International Journal of Computer Vision
Understanding the Behavior of SFM Algorithms: A Geometric Approach
International Journal of Computer Vision
A New Structure-from-Motion Ambiguity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exact Two-Image Structure from Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stochastic Approximation and Rate-Distortion Analysis for Robust Structure and Motion Estimation
International Journal of Computer Vision
Structure from Motion Using Sequential Monte Carlo Methods
International Journal of Computer Vision
The least-squares error for structure from infinitesimal motion
International Journal of Computer Vision
The Graph SLAM Algorithm with Applications to Large-Scale Mapping of Urban Structures
International Journal of Robotics Research
Globally Optimal Estimates for Geometric Reconstruction Problems
International Journal of Computer Vision
Multi-View AAM Fitting and Construction
International Journal of Computer Vision
Multi-View AAM Fitting and Construction
International Journal of Computer Vision
Global Optimization through Rotation Space Search
International Journal of Computer Vision
Machine Vision and Applications
Error analysis of 3-D motion estimation algorithms in the differential case
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Hi-index | 0.00 |
We present an analysis of SFM from the point of view of noise. This analysis results in an algorithm that is provably convergent and provably optimal with respect to a chosen norm. In particular, we cast SFM as a nonlinear optimization problem and define a bilinear projection iteration that converges to fixed points of a certain cost-function. We then show that such fixed points are "fundamental", i.e. intrinsic to the problem of SFM and not an artifact introduced by our algorithm. We classify and characterize geometrically local extrema, and we argue that they correspond to phenomena observed in visual psychophysics. Finally, we show under what conditions it is possible - given convergence to a local extremum - to "jump" to the valley containing the optimum; this leads us to suggest a representation of the scene which is invariant with respect to such local extrema.