HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Journal articles

Range-Image: Incorporating sensor topology for LiDAR point cloud processing

Abstract : This paper proposes a novel methodology for LiDAR point cloud processing that takes advantage of the implicit topology of various LiDAR sensors to derive 2D images from the point cloud while bringing spatial structure to each point. The interest of such a methodology is then proved by addressing the problems of segmentation and disocclusion of mobile objects in 3D LiDAR scenes acquired via street-based Mobile Mapping Systems (MMS). Most of the existing lines of research tackle those problems directly in the 3D space. This work promotes an alternative approach by using this 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. Using the image derived from the sensor data by exploiting the sensor topology, a semi-automatic segmentation procedure based on depth histograms is presented. Then, a variational image inpainting technique is introduced to reconstruct the areas that are occluded by objects. Experiments and validation on real data prove the effectiveness of this methodology both in terms of accuracy and speed.
Document type :
Journal articles
Complete list of metadata

Cited literature [35 references]  Display  Hide  Download

Contributor : Pierre Biasutti Connect in order to contact the contributor
Submitted on : Tuesday, April 3, 2018 - 11:39:01 AM
Last modification on : Saturday, March 5, 2022 - 3:18:03 PM


Files produced by the author(s)




Pierre Biasutti, Jean-François Aujol, Mathieu Brédif, Aurélie Bugeau. Range-Image: Incorporating sensor topology for LiDAR point cloud processing. Photogrammetric engineering and remote sensing, Asprs American Society for Photogrammetry and, 2018, 84 (6), pp.367--375. ⟨10.14358/PERS.84.6.367⟩. ⟨hal-01756975⟩



Record views


Files downloads