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A recursive structure from motion algorithm based on optical flow measurements taken from an image sequence is described. It provides estimates of surface normals in addition to 3D motion and depth. The measurements are affine motion parameters which approximate the local flow fields associated with near-planar surface patches in the scene. These are integrated over time to give estimates of the 3D parameters using an extended Kalman filter. This also estimates the camera focal length and, so, the 3D estimates are metric. The use of parametric measurements means that the algorithm is computationally less demanding than previous optical flow approaches and the recursive filter builds in a degree of noise robustness. Results of experiments on synthetic and real image sequences demonstrate that the algorithm performs well.
Structure from motion, surface normals, affine motion models, Kalman filtering.

A. Calway, "Recursive Estimation of 3D Motion and Surface Structure from Local Affine Flow Parameters," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 27, no. , pp. 562-574, 2005.
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