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
The robust estimation of multiple motions: parametric and piecewise-smooth flow fields
Computer Vision and Image Understanding
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
What Energy Functions Can Be Minimized via Graph Cuts?
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
A multi-body factorization method for motion analysis
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structure-Texture Image Decomposition--Modeling, Algorithms, and Parameter Selection
International Journal of Computer Vision
Stereo Vision in Structured Environments by Consistent Semi-Global Matching
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Disparity Flow Estimation using Orthogonal Reliability-based Dynamic Programming
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
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
Stereo Processing by Semiglobal Matching and Mutual Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-time joint disparity and disparity flow estimation on programmable graphics hardware
Computer Vision and Image Understanding
Efficient Dense Scene Flow from Sparse or Dense Stereo Data
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
An Improved Algorithm for TV-L1 Optical Flow
Statistical and Geometrical Approaches to Visual Motion Analysis
An Evaluation Approach for Scene Flow with Decoupled Motion and Position
Statistical and Geometrical Approaches to Visual Motion Analysis
Detection and Segmentation of Independently Moving Objects from Dense Scene Flow
EMMCVPR '09 Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
EMMCVPR '09 Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
A robust approach for ego-motion estimation using a mobile stereo platform
IWCM'04 Proceedings of the 1st international conference on Complex motion
A duality based approach for realtime TV-L1 optical flow
Proceedings of the 29th DAGM conference on Pattern recognition
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Dense motion and disparity estimation via loopy belief propagation
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Discontinuity-preserving computation of variational optic flow in real-time
Scale-Space'05 Proceedings of the 5th international conference on Scale Space and PDE Methods in Computer Vision
Dense estimation and object-based segmentation of the optical flow with robust techniques
IEEE Transactions on Image Processing
Go with the flow: hand trajectories in 3d via clustered scene flow
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part I
Stereoscopic scene flow for robotic assisted minimally invasive surgery
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
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
Analysis of KITTI data for stereo analysis with stereo confidence measures
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
Optimality in combinations of confidence measures for stereo vision
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
Multi-view Scene Flow Estimation: A View Centered Variational Approach
International Journal of Computer Vision
Consistent Binocular Depth and Scene Flow with Chained Temporal Profiles
International Journal of Computer Vision
Dense scene flow based on depth and multi-channel bilateral filter
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
Keep it simple and sparse: real-time action recognition
The Journal of Machine Learning Research
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Building upon recent developments in optical flow and stereo matching estimation, we propose a variational framework for the estimation of stereoscopic scene flow, i.e., the motion of points in the three-dimensional world from stereo image sequences. The proposed algorithm takes into account image pairs from two consecutive times and computes both depth and a 3D motion vector associated with each point in the image. In contrast to previous works, we partially decouple the depth estimation from the motion estimation, which has many practical advantages. The variational formulation is quite flexible and can handle both sparse or dense disparity maps. The proposed method is very efficient; with the depth map being computed on an FPGA, and the scene flow computed on the GPU, the proposed algorithm runs at frame rates of 20 frames per second on QVGA images (320脳240 pixels). Furthermore, we present solutions to two important problems in scene flow estimation: violations of intensity consistency between input images, and the uncertainty measures for the scene flow result.