DEPTH ESTIMATION FROM A SEQUENCE OF MONOCULAR IMAGES WITH KNOWN CAMERA MOTION

This paper reports an approach of computing depth maps from a monocular image sequence, under the assumption that the camera motion is known. The direct depth estimation method is combined with the optical flow based method to improve estimation accuracy. The optical flow on and 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 the moving edges but also in the internal regions. The depth estimation problem is formulated as a 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, is used to predict the depth and depth variance at each pixel in the next frame. In the estimation stage, a Kalman filter is employed to refine the predicted depth map. The resulting estimation algorithm takes into account the information from the neighboring pixels. In the smoothing stage, based on the error covariance information, morphological filtering is applied to reduce the effect of measurement noise and to fill in untrustable areas. Simulation results are provided to demonstrate the effectiveness of the proposed method.