My recent Postdoctoral position was in the GeoHistorical Data team, for the project Belle Epoque. The goal is to perform geocoding of historical addresses (aka given an input address and date, find the position on the map). The projetc heavily relies on extracting information from historical map, and use of database tricks (inheritance, indexes) to robustly and quickly find the geohistorical object that matches the input address and date. Among the difficulties, everything is fuzzy for geohistorical data : the date of course, but also the spatial position, and even the historical name (misspelling, errors, etc.).[Read More]
Inverse Procedural Street Modelling
My PhD thesis about reconstructing street for large cities using procedural methods and point clouds.
My PhD was about Inverse procedural street modelling, that is generate a street model (Chap3. road network, geometry, traffic informaton, stret objects), then fit this stret model to real life (chap5.). Real life here is expressed by massive point clouds (Chap2, Appendix A and B). Of course the fitting is automatic, but nevertheless it is essential to allow the users to perform manual edit of the street model, in a in-base shared and concurrent way (chap 4).[Read More]
Implicit Level Of Detail for Point Clouds
LOD contained in the order of points
When we created the Point Cloud Server to manage billions of points, several method processing methods were used on points. It was rapidely clear that for almost any methods, and for visualization in perticular, a way to reduce the number of points was needed. Our solution was the Implicit Level of Detail method, where points are ordered by decreasing importancy in each group of points (patch). Then reducing the number of point was a matter of taking only the n first points per patch. The method was refined, and led to the creation of a robust dimensionnality descriptor, along with its uses for classification and pre-processing. Initially regrouped in one article, this two topics (LOD and dimensionnality descriptor) were too complex to be handled together, which led to a split into two articles which are much easier to read.
Implicit Level of Detail for the Point Cloud Server
Lidar datasets now commonly reach Billions of points and are very dense. Using these point cloud becomes challenging, as the high number of points is untractabel for most applications and for visualisation. In this work we propose a new paradigm to easily get a portable geometric Level Of Details (LOD) inside a Point Cloud Server. The main idea is to not store the LOD information in an external additional file, but instead to store it implicitly by exploiting the order of the points. The point cloud is divided into groups (patches). These patches are ordered so that their order gradually provides more and more details on the patch. We demonstrate the interest of our method with several classical uses of LOD, such as visualisation of massive point cloud, algorithm acceleration, fast density peak detection and correction.[Read More]
Point Cloud Server
a server to manage massive point clouds
The Point CLoud Server was developped to manage the billions and billions of Lidar point clouds that were produced daily by IGN mobile mapping system. RAther than deal with files, which limit sharing, gestion of metadata and concurrent editing, we chose to use a database server to store groups of points (patch). The system was successfully used for the PhD work and other projects, and is still being actively researched.[Read More]
an in-base tool for street modelling
StreetGen is the method that was developped during the PhD to generate a street model including the road, the traffic information, and the street objects. Everything (base data, method, resulting street model) is contained within a database, which make it scale nicely and allow concurrent interactive editing.[Read More]