Stereo imaging is the most common passive method for producing reliable depth maps. Calibration is a crucial step for every stereo-based system, and despite all the advancements in the field, most calibrations are still done by the same tedious method using a checkerboard target. Monocular-based depth estimation methods do not require extrinsic calibration but generally achieve inferior depth accuracy.
We present a novel online self-calibration method, which makes use of both stereo and monocular depth maps to find the transformation required for extrinsic calibration by enforcing consistency between both maps. The proposed method works in a closed-loop and exploits the pre-trained networks’ global context, and thus avoids feature matching and outliers’ issues.
In addition to presenting our method using an image-based monocular depth estimation method, which can be implemented in most systems without additional changes, we also show that adding a phase-coded aperture mask leads to even better and faster convergence.

We comapred uncalibrated KITTI dataset to KITTI calibrated and DSR calibatred with our calibration:

We have presented a novel framework for self-calibration of stereo cameras by combining monocular depth cues and stereo disparity information. Our method outperforms other self-calibration techniques as demonstrated both using our prototype and using the KITTI dataset. Since it works on unconstrained images, calibration can be performed online, making it ideal for systems that are sensitive to mechanical changes (due to temperature changes or other environmental effects). Moreover, our system can automatically detect calibration errors on the fly and correct them in real-time.