  Near field depth estimation around a self driving car is an important
function that can be achieved by four wide angle fisheye cameras having a field
of view of over 180. Depth estimation based on convolutional neural networks
(CNNs) produce state of the art results, but progress is hindered because depth
annotation cannot be obtained manually. Synthetic datasets are commonly used
but they have limitations. For instance, they do not capture the extensive
variability in the appearance of objects like vehicles present in real
datasets. There is also a domain shift while performing inference on natural
images illustrated by many attempts to handle the domain adaptation explicitly.
In this work, we explore an alternate approach of training using sparse LiDAR
data as ground truth for depth estimation for fisheye camera. We built our own
dataset using our self driving car setup which has a 64 beam Velodyne LiDAR and
four wide angle fisheye cameras. To handle the difference in view points of
LiDAR and fisheye camera, an occlusion resolution mechanism was implemented. We
started with Eigen's multiscale convolutional network architecture and improved
by modifying activation function and optimizer. We obtained promising results
on our dataset with RMSE errors comparable to the state of the art results
obtained on KITTI.
