Autor segons l'article: Abdulwahab, Saddam; Rashwan, Hatem A; Garcia, Miguel Angel; Masoumian, Armin; Puig, Domenec
Departament: Enginyeria Informàtica i Matemàtiques
Autor/s de la URV: Abdulwahab, Saddam Abdulrhman Hamed / GARCIA GARCIA, MIGUEL ANGEL / Masoumian, Armin / Puig Valls, Domènec Savi
Paraules clau: Multi-scale networks Monocular depth map estimation Deep autoencoders Curvilinear saliency multi-scale networks deep autoencoders curvilinear saliency
Resum: Estimating depth from a monocular camera is a must for many applications, including scene understanding and reconstruction, robot vision, and self-driving cars. However, generating depth maps from single RGB images is still a challenge as object shapes are to be inferred from intensity images strongly affected by viewpoint changes, texture content and light conditions. Therefore, most current solutions produce blurry approximations of low-resolution depth maps. We propose a novel depth map estimation technique based on an autoencoder network. This network is endowed with a multi-scale architecture and a multi-level depth estimator that preserve high-level information extracted from coarse feature maps as well as detailed local information present in fine feature maps. Curvilinear saliency, which is related to curvature estimation, is exploited as a loss function to boost the depth accuracy at object boundaries and raise the performance of the estimated high-resolution depth maps. We evaluate our model on the public NYU Depth v2 and Make3D datasets. The proposed model yields superior performance on both datasets compared to the state-of-the-art, achieving an accuracy of 86% and showing exceptional performance at the preservation of object boundaries and small 3D structures. The code of the proposed model is publicly available at https://github.com/SaddamAbdulrhman/MDACSFB.
Àrees temàtiques: Zootecnia / recursos pesqueiros Software Matemática / probabilidade e estatística Interdisciplinar Engenharias iv Engenharias iii Engenharias i Computer science, artificial intelligence Ciências biológicas ii Ciências biológicas i Ciências ambientais Ciências agrárias i Ciência da computação Biotecnología Artificial intelligence Administração pública e de empresas, ciências contábeis e turismo
Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
Adreça de correu electrònic de l'autor: saddam.abdulwahab@urv.cat miguelangel.garciag@urv.cat domenec.puig@urv.cat saddam.abdulwahab@urv.cat armin.masoumian@urv.cat
Identificador de l'autor: 0000-0001-9972-2182 0000-0002-0562-4205
Data d'alta del registre: 2024-06-28
Versió de l'article dipositat: info:eu-repo/semantics/acceptedVersion
Enllaç font original: https://link.springer.com/article/10.1007/s00521-022-07663-x
URL Document de llicència: http://repositori.urv.cat/ca/proteccio-de-dades/
Referència a l'article segons font original: Neural Computing & Applications. 34 (19): 16423-16440
Referència de l'ítem segons les normes APA: Abdulwahab, Saddam; Rashwan, Hatem A; Garcia, Miguel Angel; Masoumian, Armin; Puig, Domenec (2022). Monocular depth map estimation based on a multi-scale deep architecture and curvilinear saliency feature boosting. Neural Computing & Applications, 34(19), 16423-16440. DOI: 10.1007/s00521-022-07663-x
DOI de l'article: 10.1007/s00521-022-07663-x
Entitat: Universitat Rovira i Virgili
Any de publicació de la revista: 2022
Tipus de publicació: Journal Publications
Repositori URV