BIASUTTI, Pierre; AUJOL, Jean-François; BRÉDIF, Mathieu; BUGEAU, Aurélie

doi:10.5194/isprs-annals-IV-1-W1-75-2017

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BIASUTTI, Pierre
Institut de Mathématiques de Bordeaux [IMB]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]

AUJOL, Jean-François
Institut de Mathématiques de Bordeaux [IMB]

BRÉDIF, Mathieu
Institut National de l'Information Géographique et Forestière [IGN] [IGN]

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Communication dans un congrès

Ce document a été publié dans

City Models, Roads and Traffic workshop (CMRT), 2017-06-06, Hannover. vol. IV-1/W1, p. 75 - 82

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This paper proposes a novel framework for the disocclusion of mobile objects in 3D LiDAR scenes aquired via street-based Mobile Mapping Systems (MMS). Most of the existing lines of research tackle this problem directly in the 3D space. This work promotes an alternative approach by using a 2D range image representation of the 3D point cloud, taking advantage of the fact that the problem of disocclusion has been intensively studied in the 2D image processing community over the past decade. First, the point cloud is turned into a 2D range image by exploiting the sensor's topology. Using the range image, a semi-automatic segmentation procedure based on depth histograms is performed in order to select the occluding object to be removed. A variational image inpainting technique is then used to reconstruct the area occluded by that object. Finally, the range image is unprojected as a 3D point cloud. Experiments on real data prove the effectiveness of this procedure both in terms of accuracy and speed.< Réduire

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variational

LiDAR

point cloud

Inpainting

disocclusion

segmentation

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Disocclusion of 3D LiDAR point clouds using range images

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