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Laboratoire d'InfoRmatique en Image et Systèmes d'information

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Laboratoire d'InfoRmatique en Image et Systèmes d'information
UMR 5205 CNRS / INSA Lyon / Université Claude Bernard Lyon 1 / Université Lumière Lyon 2 / École Centrale de Lyon
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Maximilien Guislain


PhD student

Team Geometrical Modeling, Computational Geometry, Procedural Generation
Institution Claude Bernard University of Lyon 1
Location Nautibus (Université Lyon1)
E-mail maximilien.guislain at
Contact details Publications Thesis
Subject Joint point clouds and images processing for the analysis and visualization of 3D models
Abstract Recent years saw a rapid development of city digitization technologies.
Acquisition campaigns covering entire cities are now performed using LiDAR (Light Detection And Ranging) scanners embedded aboard mobile vehicles. These acquisition campaigns yield point clouds, composed of millions of points, representing the buildings and the streets, and may also contain a set of images of the scene.
The subject developed here is the improvement of the point cloud using the information contained in the camera images. This thesis introduces several contributions to this joint improvement.
The position and orientation of acquired images are usually estimated using devices embedded with the LiDAR scanner, even if this information is inaccurate. To obtain the precise registration of an image on a point cloud, we propose a two-step algorithm which uses both Mutual Information and Histograms of Oriented Gradients. The proposed method yields an accurate camera pose, even when the initial estimations are far from the real position and orientation.
Once the images have been correctly registered, it is possible to use them to color each point of the cloud while using the variability of the point of view.
This is done by minimizing an energy taking into account the different colors associated with a particular point and the potential colors of its neighbors.
Illumination changes can also change the color assigned to a point. Notably, this color can be affected by cast shadows. These cast shadows are changing with the sun position, it is therefore necessary to detect and correct them. We propose a new method that analyzes the joint variation of the reflectance value obtained by the LiDAR and the color of the points. By detecting enough interfaces between shadow and light, we are able to characterize the luminance of the scene and to remove the cast shadows.
The last point developed in this thesis is the densification of a point cloud. Indeed, the local density of a point cloud varies and is sometimes insufficient in certain areas. We propose a directly applicable approach, using a joint bilateral filter, to increase the density of a point cloud using multiple images.
Advisor Raphaëlle Chaine
Advisor Julie Digne

Last update : 2017-07-20 14:08:17