  Spatial scene-understanding, including dense depth and ego-motion estimation,
is an important problem in computer vision for autonomous vehicles and advanced
driver assistance systems. Thus, it is beneficial to design perception modules
that can utilize crowdsourced videos collected from arbitrary vehicular onboard
or dashboard cameras. However, the intrinsic parameters corresponding to such
cameras are often unknown or change over time. Typical manual calibration
approaches require objects such as a chessboard or additional scene-specific
information. On the other hand, automatic camera calibration does not have such
requirements. Yet, the automatic calibration of dashboard cameras is
challenging as forward and planar navigation results in critical motion
sequences with reconstruction ambiguities. Structure reconstruction of complete
visual-sequences that may contain tens of thousands of images is also
computationally untenable. Here, we propose a system for practical monocular
onboard camera auto-calibration from crowdsourced videos. We show the
effectiveness of our proposed system on the KITTI raw, Oxford RobotCar, and the
crowdsourced D$^2$-City datasets in varying conditions. Finally, we demonstrate
its application for accurate monocular dense depth and ego-motion estimation on
uncalibrated videos.
