Visibility Estimation and Joint Inpainting of Lidar Depth Maps

Abstract : This paper presents a novel variational image inpainting method to solve the problem of generating, from 3-D lidar measures, a dense depth map coherent with a given color image, tackling visibility issues. When projecting the lidar point cloud onto the image plane, we generally obtain a sparse depth map, due to undersampling. Moreover , lidar and image sensor positions generally differ during acquisition , such that depth values referring to objects that are hidden from the image view point might appear with a naive projection. The proposed algorithm estimates the complete depth map, while simultaneously detecting and excluding those hidden points. It consists in a primal-dual optimization method, where a coupled total variation regularization term is included to match the depth and image gradients and a visibility indicator handles the selection of visible points. Tests with real data prove the effectiveness of the proposed strategy.
Type de document :
Communication dans un congrès
IEEE International Conference on Image Processing (ICIP), Sep 2016, Phoenix, AZ, United States
Liste complète des métadonnées


https://hal.archives-ouvertes.fr/hal-01316719
Contributeur : Marco Bevilacqua <>
Soumis le : mardi 17 mai 2016 - 15:45:00
Dernière modification le : vendredi 20 mai 2016 - 01:05:47
Document(s) archivé(s) le : vendredi 19 août 2016 - 16:54:16

Fichier

icip16_marco.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-01316719, version 1

Collections

Citation

Marco Bevilacqua, Jean-François Aujol, Mathieu Brédif, Aurélie Bugeau. Visibility Estimation and Joint Inpainting of Lidar Depth Maps. IEEE International Conference on Image Processing (ICIP), Sep 2016, Phoenix, AZ, United States. <hal-01316719>

Partager

Métriques

Consultations de
la notice

114

Téléchargements du document

197