Automatic 3D Car Model Alignment for Mixed Image-Based Rendering

Abstract : Image-Based Rendering (IBR) allows good-quality free-viewpoint navigation in urban scenes, but suffers from arti-facts on poorly reconstructed objects, e.g., reflective surfaces such as cars. To alleviate this problem, we propose a method that automatically identifies stock 3D models , aligns them in the 3D scene and performs morphing to better capture image contours. We do this by first adapting learning-based methods to detect and identify an object class and pose in images. We then propose a method which exploits all available information, namely partial and inaccurate 3D reconstruction, multi-view calibration, image contours and the 3D model to achieve accurate object alignment suitable for subsequent morphing. These steps provide models which are well-aligned in 3D and to contours in all the images of the multi-view dataset, allowing us to use the resulting model in our mixed IBR algorithm. Our results show significant improvement in image quality for free-viewpoint IBR, especially when moving far from the captured viewpoints.
Type de document :
Communication dans un congrès
2016 International Conference on 3D Vision (3DV), Oct 2016, Stanford, United States
Liste complète des métadonnées

Littérature citée [56 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-01368355
Contributeur : Team Reves <>
Soumis le : lundi 19 septembre 2016 - 14:09:20
Dernière modification le : dimanche 27 mai 2018 - 12:02:02

Fichier

final_IBRMixed_3dv.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-01368355, version 1

Collections

Citation

Rodrigo Ortiz-Cayon, Abdelaziz Djelouah, Francisco Massa, Mathieu Aubry, George Drettakis. Automatic 3D Car Model Alignment for Mixed Image-Based Rendering. 2016 International Conference on 3D Vision (3DV), Oct 2016, Stanford, United States. 〈hal-01368355〉

Partager

Métriques

Consultations de la notice

347

Téléchargements de fichiers

1171