Monocular depth estimation (MDE), which is the task of using a single image to predict scene depths, has gained considerable interest, in large part owing to the popularity of applying deep learning methods to solve “computer vision ...

This work is unique in that it emphasizes cross domain performance and ability to generalize more so than performance on the training set.

Convolutional neural networks have shown a remarkable ability to estimate depth from a single image.

This work focuses on using these feature-based sparse patterns to generate additional depth information by interpolating regions between clusters of samples that are in close proximity to each other.

During this project, state-of-the-art deep learning models have been used to estimate depth maps from a monocular RGB image applying a teacher-student learning approach.

This book gathers selected high-impact articles from the 2nd International Conference on Data Science, Machine Learning & Applications 2020.

CVPR is the premier annual computer vision event comprising the main conference and several co located workshops and short courses With its high quality and low cost, it provides an exceptional value for students, academics and industry ...

The two-volume set of LNCS 11941 and 11942 constitutes the refereed proceedings of the 8th International Conference on Pattern Recognition and Machine Intelligence, PReMI 2019, held in Tezpur, India, in December 2019.

Depth estimation from single monocular images is a theoretical challenge in computer vision as well as a computational challenge in practice.

In this work, we propose to estimate depth maps for images taken from the train driver viewpoint.