  In classical computer vision, rectification is an integral part of multi-view
depth estimation. It typically includes epipolar rectification and lens
distortion correction. This process simplifies the depth estimation
significantly, and thus it has been adopted in CNN approaches. However,
rectification has several side effects, including a reduced field of view
(FOV), resampling distortion, and sensitivity to calibration errors. The
effects are particularly pronounced in case of significant distortion (e.g.,
wide-angle fisheye cameras). In this paper, we propose a generic scale-aware
self-supervised pipeline for estimating depth, euclidean distance, and visual
odometry from unrectified monocular videos. We demonstrate a similar level of
precision on the unrectified KITTI dataset with barrel distortion comparable to
the rectified KITTI dataset. The intuition being that the rectification step
can be implicitly absorbed within the CNN model, which learns the distortion
model without increasing complexity. Our approach does not suffer from a
reduced field of view and avoids computational costs for rectification at
inference time. To further illustrate the general applicability of the proposed
framework, we apply it to wide-angle fisheye cameras with 190$^\circ$
horizontal field of view. The training framework UnRectDepthNet takes in the
camera distortion model as an argument and adapts projection and unprojection
functions accordingly. The proposed algorithm is evaluated further on the KITTI
rectified dataset, and we achieve state-of-the-art results that improve upon
our previous work FisheyeDistanceNet. Qualitative results on a distorted test
scene video sequence indicate excellent performance
https://youtu.be/K6pbx3bU4Ss.
