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

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2020

Autores

Leite, PN (Autor) Outra; Silva, RJ (Autor) Outra; Campos, DF (Autor) Outra; Pinto, AM (Autor) FEUP

Ata de Conferência Internacional

Título: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Páginas: 379-392

17th International Conference on Image Analysis and Recognition, ICIAR 2020

24 June 2020 through 26 June 2020

Indexação

Scopus - 4 Citações

Outras Informações

ID Authenticus: P-00S-DH0

DOI: 10.1007/978-3-030-50347-5_33

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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica