Joint Recursive Monocular Filtering of Camera Motion and Disparity Map
This addresses monocular 3D reconstruction for applications like robotics and autonomous driving, offering an incremental improvement over existing methods.
The paper tackles monocular scene reconstruction by developing a second-order optimal minimum energy filter that jointly estimates camera motion, disparity maps, and higher-order kinematics on a novel product Lie group. Experiments show the method achieves accurate reconstructions on-par with state-of-the-art.
Monocular scene reconstruction is essential for modern applications such as robotics or autonomous driving. Although stereo methods usually result in better accuracy than monocular methods, they are more expensive and more difficult to calibrate. In this work, we present a novel second order optimal minimum energy filter that jointly estimates the camera motion, the disparity map and also higher order kinematics recursively on a product Lie group containing a novel disparity group. This mathematical framework enables to cope with non-Euclidean state spaces, non-linear observations and high dimensions which is infeasible for most classical filters. To be robust against outliers, we use a generalized Charbonnier energy function in this framework rather than a quadratic energy function as proposed in related work. Experiments confirm that our method enables accurate reconstructions on-par with state-of-the-art.