  A 360{\deg} perception of scene geometry is essential for automated driving,
notably for parking and urban driving scenarios. Typically, it is achieved
using surround-view fisheye cameras, focusing on the near-field area around the
vehicle. The majority of current depth estimation approaches focus on employing
just a single camera, which cannot be straightforwardly generalized to multiple
cameras. The depth estimation model must be tested on a variety of cameras
equipped to millions of cars with varying camera geometries. Even within a
single car, intrinsics vary due to manufacturing tolerances. Deep learning
models are sensitive to these changes, and it is practically infeasible to
train and test on each camera variant. As a result, we present novel
camera-geometry adaptive multi-scale convolutions which utilize the camera
parameters as a conditional input, enabling the model to generalize to
previously unseen fisheye cameras. Additionally, we improve the distance
estimation by pairwise and patchwise vector-based self-attention encoder
networks. We evaluate our approach on the Fisheye WoodScape surround-view
dataset, significantly improving over previous approaches. We also show a
generalization of our approach across different camera viewing angles and
perform extensive experiments to support our contributions. To enable
comparison with other approaches, we evaluate the front camera data on the
KITTI dataset (pinhole camera images) and achieve state-of-the-art performance
among self-supervised monocular methods. An overview video with qualitative
results is provided at https://youtu.be/bmX0UcU9wtA. Baseline code and dataset
will be made public.
