Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
The Geometry of Multiple Images: The Laws That Govern The Formation of Images of A Scene and Some of Their Applications
International Journal of Computer Vision
Joint Disparity and Motion Field Estimation in Stereoscopic Image Sequences
ICPR '96 Proceedings of the 1996 International Conference on Pattern Recognition (ICPR '96) Volume I - Volume 7270
SURFACES FOR COMPUTER-AIDED DESIGN OF SPACE FORMS
SURFACES FOR COMPUTER-AIDED DESIGN OF SPACE FORMS
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Towards Ultimate Motion Estimation: Combining Highest Accuracy with Real-Time Performance
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Efficient Belief Propagation for Early Vision
International Journal of Computer Vision
Edge-preserving Simultaneous Joint Motion-Disparity Estimation
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Multi-View Stereo Reconstruction and Scene Flow Estimation with a Global Image-Based Matching Score
International Journal of Computer Vision
A Variational Model for the Joint Recovery of the Fundamental Matrix and the Optical Flow
Proceedings of the 30th DAGM symposium on Pattern Recognition
Efficient Dense Scene Flow from Sparse or Dense Stereo Data
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
An Evaluation Approach for Scene Flow with Decoupled Motion and Position
Statistical and Geometrical Approaches to Visual Motion Analysis
EMMCVPR '09 Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Dense motion and disparity estimation via loopy belief propagation
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Dense and accurate spatio-temporal multi-view stereovision
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
Dense estimation and object-based segmentation of the optical flow with robust techniques
IEEE Transactions on Image Processing
Using active illumination for accurate variational space-time stereo
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
Dense versus Sparse Approaches for Estimating the Fundamental Matrix
International Journal of Computer Vision
Lightweight binocular facial performance capture under uncontrolled lighting
ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH Asia 2012
A tensor voting approach for multi-view 3d scene flow estimation and refinement
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
On the evaluation of scene flow estimation
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
Consistent Binocular Depth and Scene Flow with Chained Temporal Profiles
International Journal of Computer Vision
On-set performance capture of multiple actors with a stereo camera
ACM Transactions on Graphics (TOG)
Local scene flow by tracking in intensity and depth
Journal of Visual Communication and Image Representation
International Journal of Computer Vision
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We present a novel variational method for the simultaneous estimation of dense scene flow and structure from stereo sequences. In contrast to existing approaches that rely on a fully calibrated camera setup, we assume that only the intrinsic camera parameters are known. To couple the estimation of motion, structure and geometry, we propose a joint energy functional that integrates spatial and temporal information from two subsequent image pairs subject to an unknown stereo setup. We further introduce a normalisation of image and stereo constraints such that deviations from model assumptions can be interpreted in a geometrical way. Finally, we suggest a separate discontinuity-preserving regularisation to improve the accuracy. Experiments on calibrated and uncalibrated data demonstrate the excellent performance of our approach. We even outperform recent techniques for the rectified case that make explicit use of the simplified geometry.