Estimation of 3D Object Structure, Motion and Rotation Based on 4D Affine Optical Flow Using a Multi-camera Array

摘要

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 ID 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.