BLETTERER , Arnaud

With the evolution of 3D acquisition devices, point clouds have now become an essential representation of digitized scenes. Recent systems are able to capture several hundreds of millions of points in a single acquisition. As multiple acquisitions are necessary to capture the geometry of large-scale scenes, a historical site for example, we...

We present an original workflow for structuring a point cloud generated from several scans. Our representation is based on a set of local graphs. Each graph is constructed from the depth map provided by each scan. The graphs are then connected together via the overlapping areas, and careful consideration of the redundant points in these regions...

We show how a local graph-based representation can be used for structuring 3D point clouds provided by multiple LiDAR acquisitions. Those graphs are defined from the depth maps generated by the sensors, and connected together to get a single and global representation of the scanned site. Experimental results show that this structure is...

National audience

We present an original method for resampling point clouds provided by aggregation of LiDAR acquisitions, using a Poisson-disk sampling. It is based on a set of local graphs connected together to get a global representation of the acquired site. This approach allows us to improve the quality of gigantic 3D scans such as the ones generated for...

International audience

Nowadays, LiDAR scanners are able to capture complex scenes of real life, leading to extremely detailed point clouds. However, the amount of points acquired (several billions) and their distribution raise the problem of sampling a surface optimally. Indeed, these point clouds finely describe the acquired scene, but also exhibit numerous defects...

Nous présentons une méthode originale de reconstruction de surfaces à partir de nuages de points. Notre méthode est spécifiquementdédiée aux nuages de points massifs représentant des scènes 3D complexes. Contrairement aux approches classiques qui travaillent directementdans le domaine 3D, notre méthode travaille sur les cartes de profondeur,...

Nowadays, LiDAR scanners are able to digitize very wide historical sites, leading to point clouds composed of billions of points. These point clouds are able to describe very small objects or elements disseminated in these sites, but also exhibit numerous defects in terms of sampling quality. Moreover, they sometimes contain too many samples to...