As an essential component for many autonomous driving and robotic activities such as ego-
motion estimation, obstacle avoidance and scene understanding, monocular depth …

Modern autonomous mobile robots require a strong understanding of their surroundings in
order to safely operate in cluttered and dynamic environments. Monocular depth estimation …

Environment perception by computing the depth is a key task for unmanned aerial vehicle
(UAV) type systems. Due to the limited load they can carry, most drones are equipped with a …

In recent years, self-supervised methods for monocular depth estimation has rapidly become
an significant branch of depth estimation task, especially for autonomous driving …

Inferring the depth of images is a fundamental inverse problem within the field of Computer
Vision since depth information is obtained through 2D images, which can be generated from …

While state-of-the-art monocular depth estimation approaches achieve impressive results in
ideal settings, they are highly unreliable under challenging illumination and weather …

In current decades, significant advancements in robotics engineering and autonomous
vehicles have improved the requirement for precise depth measurements. Depth estimation …

With a great success of the generative model via deep neural networks, monocular depth
estimation has been actively studied by exploiting various encoder-decoder architectures …

Estimating depth from a single image represents an attractive alternative to more traditional
approaches leveraging multiple cameras. In this field, deep learning yielded outstanding …

In this paper, we propose a Bidirectional Attention Network (BANet), an end-to-end
framework for monocular depth estimation (MDE) that addresses the limitation of effectively …