We investigate the recovery of 3D motion and structure from the stereo images of a stationary environment. A Kalman filter-based framework is proposed for the reconstruction of 3D structure from multiple visual cues, through the integration of image motion and stereo disparity with the shading flow that is induced by the rotational motion of the source. This allows the exploitation of available visual cues in the common scenario involving the coupled motion of artificial source(s) and stereo cameras that are installed on mobile submersible vehicles. Utilizing shading flow with the image motion leads to devising a more robust 3D motion estimation algorithm, in addition to the critical role in depth recovery/refinement by constraining the local surface gradients. Collectively, use of multiple cues enhances robustness with respect to perturbation in any of the cues. Results of experiments with real imagery are presented to evaluate the performance of the proposed algorithm.
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