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Fusion-based depth estimation from a sequence of monocular images

This paper reports the development of a general depth estimation system directly using image sequence. We combine the direct depth estimation method with the optical flow based method. More specifically, the optical flow on or near moving edges are computed using a correlation technique. The optical flow information is then fused with gradient information to estimate depth not only on moving edges but also in internal regions. The depth estimation problem is formulated as a discrete Kalman filter problem and is solved in three stages. In the prediction stage, the depth map estimated for the current frame, together with knowledge of the camera motion, are used to predict the depth variance at each pixel in the next frame. In the estimation stage, a vector-version of Kalman filter formulation is adopted and then simplified under the assumption of a diagonized error covariance. The resulting estimation algorithm takes into account of the information from the neighboring pixels and therefore is much more robust than the scalar-version Kalman filter implementation. In the smoothing stage, morphological filtering is applied to the estimated depth map to reduce the measurement noise and fill in the untrustable areas based on the error covariance information. Simulation results illustrate the effectiveness of the presented method.