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Monocular scene reconstruction is essential for modern applications such asrobotics or autonomous driving. Although stereo methods usually result inbetter accuracy than monocular methods, they are more expensive and moredifficult to calibrate. In this work, we present a novel second order optimalminimum energy filter that jointly estimates the camera motion, the disparitymap and also higher order kinematics recursively on a product Lie groupcontaining a novel disparity group. This mathematical framework enables to copewith non-Euclidean state spaces, non-linear observations and high dimensionswhich is infeasible for most classical filters. To be robust against outliers,we use a generalized Charbonnier energy function in this framework rather thana quadratic energy function as proposed in related work. Experiments confirmthat our method enables accurate reconstructions on-par with state-of-the-art.

Joint Recursive Monocular Filtering of Camera Motion and Disparity Map