Publication
CamLessMonoDepth: Monocular Depth Estimation with Unknown Camera Parameters
Shyam Chanduri; Zeeshan Khan Suri; Igor Vozniak; Christian Müller
In: British Machine Vision Conference. British Machine Vision Conference (BMVC-2021), The British Machine Vision Association and Society for Pattern Recognition, November 22-25, Online, The British Machine Vision Association, 2021.
Abstract
Perceiving 3D information is of paramount importance in many applications of com-
puter vision. Recent advances in monocular depth estimation have shown that gaining
such knowledge from a single camera input is possible by training deep neural networks
to predict inverse depth and pose, without the necessity of ground truth data. The major-
ity of such approaches, however, require camera parameters to be fed explicitly during
training. As a result, image sequences from wild cannot be used during training. While
there exist methods which also predict camera intrinsics, their performance is not on
par with novel methods taking camera parameters as input. In this work, we propose a
method for implicit estimation of pinhole camera intrinsics along with depth and pose, by
learning from monocular image sequences alone. In addition, by utilizing efficient sub-
pixel convolutions, we show that high fidelity depth estimates can be obtained. We also
embed pixel-wise uncertainty estimation into the framework, to emphasize the possible
applicability of this work in practical domain. Finally, we demonstrate the possibility of
accurate prediction of depth information without prior knowledge of camera intrinsics,
while outperforming the existing state-of-the-art approaches on KITTI benchmark.