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 …

Deep-learning-based localization and map** approaches have recently emerged as a
new research direction and receive significant attention from both industry and academia …

This paper tackles the challenges of self-supervised monocular depth estimation in indoor
scenes caused by large rotation between frames and low texture. We ease the learning …

Extracting 3D information from a single optical image is very attractive. Recently emerging
self-supervised methods can learn depth representations without using ground truth depth …

Dense scene reconstruction for photo-realistic view synthesis has various applications, such
as VR/AR, autonomous vehicles. However, most existing methods have difficulties in large …

With the development of computational intelligence algorithms, unsupervised monocular
depth and pose estimation framework, which is driven by warped photometric consistency …

Estimation of depth in two-dimensional images is among the challenging topics in Computer
Vision. This is a well-studied but also an ill-posed problem, which has long been the focus of …

Self-supervised monocular depth estimation has been a challenging task in computer vision
for a long time, and it relies on only monocular or stereo video for its supervision. To address …

We present a novel approach designed to address the complexities posed by challenging,
out-of-distribution data in the single-image depth estimation task. Starting with images that …

It is attractive to extract plausible 3-D information from a single 2-D image, and self-
supervised learning has shown impressive potential in this field. However, when only …