Closed form solutions to image flow equations for planar surfaces in motion
Computer Vision, Graphics, and Image Processing
Performance of optical flow techniques
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-View Stereo Reconstruction and Scene Flow Estimation with a Global Image-Based Matching Score
International Journal of Computer Vision
Range Flow for Varying Illumination
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Efficient Dense Scene Flow from Sparse or Dense Stereo Data
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Continuous Global Optimization in Multiview 3D Reconstruction
International Journal of Computer Vision
An Affine Optical Flow Model for Dynamic Surface Reconstruction
Statistical and Geometrical Approaches to Visual Motion Analysis
Optimal filters for extended optical flow
IWCM'04 Proceedings of the 1st international conference on Complex motion
Simultaneous estimation of surface motion, depth and slopes under changing illumination
Proceedings of the 29th DAGM conference on Pattern recognition
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In this paper we extend a standard affine optical flow model to 4D and present how affine parameters can be used for estimation of 3D object structure, 3D motion and rotation using a 1D camera grid. Local changes of the projected motion vector field are modelled not only on the image plane as usual for affine optical flow, but also in camera displacement direction, and in time. We identify all parameters of this 4D fully affine model with terms depending on scene structure, scene motion, and camera displacement. We model the scene by planar, translating, and rotating surface patches and project them with a pinhole camera grid model. Imaged intensities of the projected surface points are then modelled by a brightness change model handling illumination changes. Experiments demonstrate the accuracy of the new model. It outperforms not only 2D affine optical flow models but range flow for varying illumination. Moreover we are able to estimate surface normals and rotation parameters. Experiments on real data of a plant physiology experiment confirm the applicability of our model.