A major obstacle to the development of effective monocular depth estimation algorithms is
the difficulty in obtaining high-quality depth data that corresponds to collected RGB images …

Despite the recent success of learning-based monocular depth estimation algorithms and
the release of large-scale datasets for training, the methods are limited to depth map …

Accurate depth maps are essential in various applications, such as autonomous driving,
scene reconstruction, point-cloud creation, etc. However, monocular-depth estimation (MDE) …

Depth estimation is a classical topic in computer vision, however, inferring the depth of a
scene from a single image remains an extremely difficult problem. In this paper, a non …

This paper aims to address the problem of supervised monocular depth estimation. We start
with a meticulous pilot study to demonstrate that the long-range correlation is essential for …

Monocular depth estimation can play an important role in addressing the issue of deriving
scene geometry from 2D images. It has been used in a variety of industries, including robots …

Considerable technological advancements have been achieved in autonomous navigation,
which has applications in various fields such as robotics, autonomous vehicles, and …

We present a lightweight encoder-decoder architecture for monocular depth estimation,
specifically designed for embedded platforms. Our main contribution is the Guided …

Ground-truth RGBD data are fundamental for a wide range of computer vision applications;
however, those labeled samples are difficult to collect and time-consuming to produce. A …

While significant progress has been made in monocular depth estimation with Convolutional
Neural Networks (CNNs) extracting absolute features, such as edges and textures, the depth …