Visibility Estimation and Joint Inpainting of Lidar Depth Maps

Marco Bevilacqua (1, 2, 3) , Jean-François Aujol (1) , Mathieu Brédif (3) , Aurélie Bugeau (2)

1 IMB - Institut de Mathématiques de Bordeaux
2 LaBRI - Laboratoire Bordelais de Recherche en Informatique
3 MATIS - Méthodes d'Analyses pour le Traitement d'Images et la Stéréorestitution

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.

Mots clés: Inpainting; Total Variation; Depth Maps; Lidar; Point Cloud; Visibility

Domaines: Traitement des images [eess.IV]

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⟩