Go to:
Logótipo
Comuta visibilidade da coluna esquerda
Você está em: Start > Publications > View > Dense disparity maps from rgb and sparse depth information using deep regression models
Publication

Publications

Dense disparity maps from rgb and sparse depth information using deep regression models

Title
Dense disparity maps from rgb and sparse depth information using deep regression models
Type
Article in International Conference Proceedings Book
Year
2020
Authors
Leite, PN
(Author)
Other
The person does not belong to the institution. The person does not belong to the institution. The person does not belong to the institution. View Authenticus page Without ORCID
Silva, RJ
(Author)
Other
The person does not belong to the institution. The person does not belong to the institution. The person does not belong to the institution. View Authenticus page Without ORCID
Campos, DF
(Author)
Other
The person does not belong to the institution. The person does not belong to the institution. The person does not belong to the institution. View Authenticus page Without ORCID
Pinto, AM
(Author)
FEUP
View Personal Page You do not have permissions to view the institutional email. Search for Participant Publications View Authenticus page Without ORCID
Conference proceedings International
Pages: 379-392
17th International Conference on Image Analysis and Recognition, ICIAR 2020
24 June 2020 through 26 June 2020
Indexing
Other information
Authenticus ID: P-00S-DH0
Abstract (EN): A dense and accurate disparity map is relevant for a large number of applications, ranging from autonomous driving to robotic grasping. Recent developments in machine learning techniques enable us to bypass sensor limitations, such as low resolution, by using deep regression models to complete otherwise sparse representations of the 3D space. This article proposes two main approaches that use a single RGB image and sparse depth information gathered from a variety of sensors/techniques (stereo, LiDAR and Light Stripe Ranging (LSR)): a Convolutional Neural Network (CNN) and a cascade architecture, that aims to improve the results of the first. Ablation studies were conducted to infer the impact of these depth cues on the performance of each model. The models trained with LiDAR sparse information are the most reliable, achieving an average Root Mean Squared Error (RMSE) of 11.8 cm on our own Inhouse dataset; while the LSR proved to be too sparse of an input to compute accurate predictions on its own. © Springer Nature Switzerland AG 2020.
Language: English
Type (Professor's evaluation): Scientific
Documents
We could not find any documents associated to the publication.
Recommend this page Top
Copyright 1996-2025 © Faculdade de Direito da Universidade do Porto  I Terms and Conditions  I Acessibility  I Index A-Z
Page created on: 2025-08-07 at 13:49:08 | Privacy Policy | Personal Data Protection Policy | Whistleblowing