3D shape representation with spatial probabilistic distribution of intrinsic shape keypoints

Autor: Vijaya K. Ghorpade, Paul Checchin, Laurent Malaterre, Laurent Trassoudaine

Publikováno v: EURASIP Journal on Advances in Signal Processing, Vol 2017, Iss 1, Pp 1-22 (2017)

Abstract The accelerated advancement in modeling, digitizing, and visualizing techniques for 3D shapes has led to an increasing amount of 3D models creation and usage, thanks to the 3D sensors which are readily available and easy to utilize. As a res

Externí odkaz: https://doaj.org/article/3f7f2aa793c9489d98330e2c42e578ba

Segmentation Based Classification of 3D Urban Point Clouds: A Super-Voxel Based Approach with Evaluation

Autor: Laurent Trassoudaine, Paul Checchin, Ahmad Kamal Aijazi

Publikováno v: Remote Sensing, Vol 5, Iss 4, Pp 1624-1650 (2013)

Segmentation and classification of urban range data into different object classes have several challenges due to certain properties of the data, such as density variation, inconsistencies due to missing data and the large data size that require heavy

Externí odkaz: https://doaj.org/article/7e7036ee01f0472f91977052aeb5a180

Automatic Detection and Parameter Estimation of Trees for Forest Inventory Applications Using 3D Terrestrial LiDAR

Autor: Ahmad K. Aijazi, Paul Checchin, Laurent Malaterre, Laurent Trassoudaine

Publikováno v: Remote Sensing, Vol 9, Iss 9, p 946 (2017)

Forest inventory plays an important role in the management and planning of forests. In this study, we present a method for automatic detection and estimation of trees, especially in forest environments using 3D terrestrial LiDAR data. The proposed me

Externí odkaz: https://doaj.org/article/dc4edbd7346c42bb8eaa5f3bfe993350

DATA-DRIVEN MODELING OF BUILDING INTERIORS FROM LIDAR POINT CLOUDS

Autor: Laurent Trassoudaine, Florent Dupont, Julia Sanchez, Florence Denis, Paul Checchin

Publikováno v: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol V-2-2020, Pp 395-402 (2020)
ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences
ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020, V-2-2020, pp.395-402. ⟨10.5194/isprs-annals-V-2-2020-395-2020⟩

This paper deals with 3D modeling of building interiors from point clouds captured by a 3D LiDAR scanner. Indeed, currently, the building reconstruction processes remain mostly manual. While LiDAR data have some specific properties which make the rec

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9e9f095ed57ef4b0e397829c32950870
https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-2-2020/395/2020/isprs-annals-V-2-2020-395-2020.pdf

Robust normal vector estimation in 3D point clouds through iterative principal component analysis

Autor: Paul Checchin, Laurent Trassoudaine, David Coeurjolly, Florence Denis, Julia Sanchez, Florent Dupont

Publikováno v: ISPRS Journal of Photogrammetry and Remote Sensing
ISPRS Journal of Photogrammetry and Remote Sensing, Elsevier, 2020, 163, pp.18-35. ⟨10.1016/j.isprsjprs.2020.02.018⟩
ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 163, pp.18-35. ⟨10.1016/j.isprsjprs.2020.02.018⟩

This paper introduces a robust normal vector estimator for point cloud data. It can handle sharp features as well as smooth areas. Our method is based on the inclusion of a robust estimator into a Principal Component Analysis in the neighborhood of t

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::90c8e739f2d3689d8e42313cbb0f782e
https://hal.archives-ouvertes.fr/hal-02514851/file/normal_estimation.pdf