Autor según el artículo: Abdulwahab, Saddam; Rashwan, Hatem A.; Garcia, Miguel Angel; Jabreel, Mohammed; Chambon, Sylvie; Puig, Domenec;

Departamento: Enginyeria Informàtica i Matemàtiques

Autor/es de la URV: Abdellatif Fatahallah Ibrahim Mahmoud, Hatem / GARCIA GARCIA, MIGUEL ANGEL / Puig Valls, Domènec Savi

Palabras clave: Three-dimensional displays Solid modeling Pose estimation Generative adversarial networks Face Depth prediction Deep learning Color

Resumen: Estimating a depth map and, at the same time, predicting the 3D pose of an object from a single 2D color image is a very challenging task. Depth estimation is typically performed through stereo vision by following several time-consuming stages, such as epipolar geometry, rectification and matching. Alternatively, when stereo vision is not useful or applicable, depth relations can be inferred from a single image as studied in this paper. More precisely, deep learning is applied in order to solve the problem of estimating a depth map from a single image. Then, that map is used for predicting the 3D pose of the main object depicted in the image. The proposed model consists of two successive neural networks. The first network is based on a Generative Adversarial Neural network (GAN). It estimates a dense depth map from the given color image. A Convolutional Neural Network (CNN) is then used to predict the 3D pose from the generated depth map through regression. The main difficulty to jointly estimate depth maps and 3D poses using deep networks is the lack of training data with both depth and viewpoint annotations. This contribution assumes a cross-domain training procedure with 3D CAD models corresponding to objects appearing in real images in order to render depth images from different viewpoints. These rendered images are then used to guide the GAN network to learn the mapping from the image domain to the depth domain. By exploiting the dataset as a source of training data, the proposed model outperforms state-of-the-art models on the PASCAL 3D+ dataset. The code of the proposed model is publicly available at https://github.com/SaddamAbdulrhman/Depth-and-Viewpoint-Estimation/tree/master.

Áreas temáticas: Media technology Matemática / probabilidade e estatística Engineering, electrical & electronic Engenharias iv Engenharias iii Electrical and electronic engineering Ciência da computação Administração pública e de empresas, ciências contábeis e turismo

Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/

Direcció de correo del autor: hatem.abdellatif@urv.cat miguelangel.garciag@urv.cat domenec.puig@urv.cat

Identificador del autor: 0000-0001-9972-2182 0000-0002-0562-4205

Fecha de alta del registro: 2023-02-19

Versión del articulo depositado: info:eu-repo/semantics/acceptedVersion

Enlace a la fuente original: https://ieeexplore.ieee.org/document/8990121

URL Documento de licencia: http://repositori.urv.cat/ca/proteccio-de-dades/

Referencia al articulo segun fuente origial: Ieee Transactions On Circuits And Systems For Video Technology. 30 (9): 2947-2958

Referencia de l'ítem segons les normes APA: Abdulwahab, Saddam; Rashwan, Hatem A.; Garcia, Miguel Angel; Jabreel, Mohammed; Chambon, Sylvie; Puig, Domenec; (2020). Adversarial Learning for Depth and Viewpoint Estimation From a Single Image. Ieee Transactions On Circuits And Systems For Video Technology, 30(9), 2947-2958. DOI: 10.1109/TCSVT.2020.2973068

DOI del artículo: 10.1109/TCSVT.2020.2973068

Entidad: Universitat Rovira i Virgili

Año de publicación de la revista: 2020

Tipo de publicación: Journal Publications