Stéphane Guinard

Université Laval | ULAVAL · Faculty of Forestry and Geomatics

We present a new method for the simplification of 3D point clouds for digital twin city models. Such data usually contains a large amount of redundant information, noise and outliers. This implies that most of subsequent processing tasks are costly both in terms of processing times and hardware infrastructure. The core idea of this paper is that mo...

Railroad environments are peculiar, as they combine dense urban areas, along with rural parts. They also display a very specific spatial organization. In order to monitor a railway network a at country scale, LiDAR sensors can be equipped on a running train, performing a full acquisition of the network. Then most processing steps are manually done....

Detecting planar structures in point clouds is a very central step of the point cloud processing pipeline as many Lidar scans, in particular in anthropic environments, present such planar structures. Many improvements have been proposed to RANSAC and the Hough transform, the two major types of plane detection methods. An important limitation howeve...

Thanks to their ever improving resolution and accessibility, \gls{acr::lidar} sensors are increasingly used for mapping cities. Indeed, these sensors are able to efficiently capture high-density scans, which can then be used to produce geometrically detailed reconstructions of complex scenes. However, such reconstruction requires organizing the sca...

We introduce a new method for the piecewise-planar approximation of 3D data, including point clouds and meshes. Our method is designed to operate on large datasets (e.g. millions of vertices) containing planar structures, which are very frequent in anthropic scenes. Our approach is also adaptive to the local geometric complexity of the input data....

We introduce a new method for the piecewise-planar approximation of 3D data, including point clouds and meshes. Our method is designed to operate on large datasets (e.g. millions of vertices) containing planar structures, which are very frequent in an-thropic scenes. Our approach is also adaptive to the local geometric complexity of the input data....

We propose a new method for the reconstruction of simplicial complexes (combining points, edges and triangles) from 3D points clouds from Mobile Laser Scanning (MLS). Our method uses the inherent topology of the MLS sensor to define a spatial adjacency relationship between points. We then investigate each posible connexion between adjacent points,...

We propose a new method for the reconstruction of simplicial complexes (combining points, edges and triangles) from 3D point clouds from Mobile Laser Scanning (MLS). Our main goal is to produce a reconstruction of a scene that is adapted to the local geometry of objects. Our method uses the inherent topology of the MLS sensor to define a spatial ad...

We propose a new method for the reconstruction of simplicial complexes (combining points, edges and triangles) from 3D point clouds from Mobile Laser Scanning (MLS). Our main goal is to produce a reconstruction of a scene that is adapted to the local geometry of objects. Our method uses the inherent topology of the MLS sensor to define a spatial ad...

We propose a new method for the reconstruction of sim-plicial complexes (combining points, edges and triangles) from 3D point clouds from Mobile Laser Scanning (MLS). Our method uses the inherent topology of the MLS sensor to define a spatial adjacency relationship between points. We then investigate each possible connexion between adjacent points,...

We propose a new method for the reconstruction of sim-plicial complexes (combining points, edges and triangles) from 3D point clouds from Mobile Laser Scanning (MLS). Our method uses the inherent topology of the MLS sensor to define a spatial adjacency relationship between points. We then investigate each possible connexion between adjacent points,...

Nous traitons le problème de la classification sémantique de nuages de points 3D LIDAR pour les scènes urbaines à partir d'un jeu d'apprentissage limité. Nous introdui-sons un modèle de segmentation non paramétrique pour les scènes urbaines formées par des objets anthropiques de formes simples. Notre modèle segmente la scène en ré-gions géométrique...

We consider the problem of the semantic classification of 3D LiDAR point clouds obtained from urban scenes when the training set is limited. We propose a non-parametric segmentation model for urban scenes composed of anthropic objects of simple shapes, partionning the scene into geometrically-homogeneous segments which size is determined by the loc...