Year: 2025
Type: article
Abstract: Depth estimation is the computer vision task that assigns a distance between the camera and each pixel in an image. This paper focuses on monocular metric depth estimation in videos, which infers a distance in metric units using a single RGB camera. Considering its applications, robotics systems and environmental mapping arise as practical areas that can make extensive usage of these techniques. As a case study for indoor robotics, the ICL ground robot dataset obtained by video footage in graphic simulation was used for experiments. A comparison was made considering the results and requirements of data acquisition needed for different deep learning models, presenting self-supervised and supervised methods available in literature and being the first work to present a depth estimation benchmark for the chosen dataset.
Authors: Fábio Leandro Vizzotto, Marcos D'Addio de Moura, Vinicius Carbonezi de Souza, Cides Semprebom Bezerra, Guilherme Ribeiro Sales, Valentino Corso, Luiz Eduardo Pita Mercês Almeida, Douglas Henrique Siqueira Abreu
Language: English
Topic: Advanced Vision and Imaging
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4407772840