Visual odometry (VO) is a prevalent way to deal with the relative localization problem, which
is becoming increasingly mature and accurate, but it tends to be fragile under challenging …

Self-supervised monocular depth estimation that does not require ground truth for training
has attracted attention in recent years. It is of high interest to design lightweight but effective …

We address the problem of estimating a high quality dense depth map from a single RGB
input image. We start out with a baseline encoder-decoder convolutional neural network …

Estimating depth from single RGB images and videos is of widespread interest due to its
applications in many areas, including autonomous driving, 3D reconstruction, digital …

Estimating accurate depth from a single image is challenging because it is an ill-posed
problem as infinitely many 3D scenes can be projected to the same 2D scene. However …

Monocular depth estimation, which plays a crucial role in understanding 3D scene
geometry, is an ill-posed prob-lem. Recent methods have gained significant improvement by …

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 …

Single-view depth prediction is a fundamental problem in computer vision. Recently, deep
learning methods have led to significant progress, but such methods are limited by the …

While convolutional neural networks have shown a tremendous impact on various computer
vision tasks, they generally demonstrate limitations in explicitly modeling long-range …

A growing interest in autonomous driving (AD) and intelligent vehicles (IVs) is fueled by their
promise for enhanced safety, efficiency, and economic benefits. While previous surveys …