Deep neural models, in recent years, have been successful in almost every field, even
solving the most complex problem statements. However, these models are huge in size with …

Depth information is important for autonomous systems to perceive environments and
estimate their own state. Traditional depth estimation methods, like structure from motion …

Self-supervised monocular depth estimation is an attractive solution that does not require
hard-to-source depth la-bels for training. Convolutional neural networks (CNNs) have …

Self-supervised monocular depth estimation presents a powerful method to obtain 3D scene
information from single camera images, which is trainable on arbitrary image sequences …

Depth estimation from monocular images is pivotal for real-world visual perception systems.
While current learning-based depth estimation models train and test on meticulously curated …

Photometric loss is widely used for self-supervised depth and egomotion estimation.
However, the loss landscapes induced by photometric differences are often problematic for …

Augmented reality (AR) has proven to be an invaluable interactive medium to reduce
cognitive load by bridging the gap between the task-at-hand and relevant information by …

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 …

Self-supervised paradigms for monocular depth estimation are very appealing since they do
not require ground truth annotations at all. Despite the astonishing results yielded by such …