| My research topics focus on geometric modeling and analysis of shapes, objects and scenes that exhibit structural regularities, typically man-made objects and urban scenes. |
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Spatial point process |
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Spatial point processes describe random configurations of points in a continuous bounded domain. By assigning a parametric object to each point, these stochastic geometry models
become a powerful tool to extract objects with complex spatial interactions from images and 3D data. I am particularly interested in extending point processes to sample more complex structures as triangulations and planar graphs. I am also interested by the design of efficient and scalable samplers for large scale scenes.
Main contributions: Delaunay point processes [PAMI20] Jump-Diffusion sampler for multiple types of parametric objects [BMVC08, PAMI10] Monte Carlo sampler operating in parallel on large scenes [ECCV12, IJCV14] Point process to sample planar graphs [CVPR13] Model to extract linear tree structures in remote sensing images[JPRS17] Model to reconstruct Lidar waveforms [IP10] |
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City modeling |
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City modeling is a very challenging problem with geometrically different objects, acquisition constraints and scalability issues. City modeling requires analyzing not only the geometry, but also the semantics and structure of objects.
Urban objects of interest include building, vegetation and road mainly. I am especially interested in designing scalable methods for airborne and satellite data.
Main contributions: 3D-block assembling from DSM [CVPR08, PAMI10] Planimetric arrangement from Lidar [ICCV11, IJCV12] Voronoi cell fusion from satellite stereo images [ECCV16] LOD generation by 3D discrete arrangement [TOG16] Learning approach to evaluate city models [PERS19] Roof skeletization [JPRS08] |
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Geometric shape detection |
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The goal of shape detection consists in turning a large amount of sampling data into a higher level representation based on simple geometric shapes. Shape detection is typically used as a prior step in a large variety of tasks ranging
from surface reconstruction to data registration. I am particularly interested in geometric regularities and structural scale-spaces that govern man-made objects.
Main contributions: Planar shape detection and regularization in tandem [CGF15] Image partitioning by Voronoi diagram [CVPR15] Planar shape detection at structural scales [CVPR18] Polygonal partitioning by kinetic framework [CVPR18] Approximating shapes in images with polygons [CVPR20] |
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Surface reconstruction |
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Surface reconstruction is a classical problem in computer vision and
computational geometry. The goal is to interpolate input points by a surface mesh. Input points mainly come from Multi-View Stereo
imagery and Laser scans. My research directions mainly focus on the formulation of new surface quality measures combining different criteria as geometric accuracy, representation compexity,
and structural constraints. I am also interested in piecewise-planar and hybrid representations.
Main contributions: Reconstruction by point set structuring [EG13] Multi-view stereo reconstruction by multiple shape sampling [CVPR10, PAMI13] Reconstruction of indoor scenes by multi-layered 2D-arrangements [JPRS14] Reconstruction by a connect-and-slice approach [CVPR20] 3D Kinetic data structure[TOG20] |
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Surface approximation |
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Surface approximation typically consists in remeshing a input surface by imposing constraints on the geometry, topology, complexity or structure of the output solution. I am particularly interested in designing methods that preserve structural considerations.
Main contributions: Mesh decimation driven by planar shapes [CGF15] Constrained remeshing in Zometool models [GM14] |
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Object and scene classification |
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Object and scene classification is a traditional problem in vision and machine learning. I am interested in designing classification methods both fast and scalable by exploiting geometric information at a high level.
Main contributions: Classification of textured meshes for urban landscapes [JPRS17] Classification of man-made objects by planar shape analysis [ISPRS16] Pyramid scene parsing Network for 3D point cloud classification [JPRS19] Low-power neural networks for onboard satellite image classification [ICCVW19] |
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Sketch interpretation |
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Sketches drawn by designers are traditionally bitmap images that need to be converted into vector graphics for better rendering quality, editability and compactness. I am interested in vectorizing line-drawings and multilayered color images using non-local analysis, as well as interpreting sketches in 3D using geometric priors.
Main contributions: Vectorization of line-drawings by Bezier curve networks [SIGGRAPH16] 3D reconstruction of line-drawings in a multiview stereo context [CVPR15] Image Abstraction by Layered Linear Color Gradients[SIGGRAPH ASIA17] |
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Student supervision |
| Mulin Yu | Remeshing BIM models [PhD., 2019-2022] |
| Gaetan Bahl | Low-power neural networks [PhD., 2019-2022] |
| Julien Vuillamy | City reconstruction from multi-sourced data [PhD., 2018-2021, supervised with Pierre Alliez] |
| Muxingzi Li | Indoor modeling [PhD., 2018-2021] |
| Jean-Philippe Bauchet | Urban scene reconstruction [PhD., 2016-2019] |
| Oussama Ennafii | Geometric quality of city models [PhD., 2016-2019, supervised with Clement Mallet] |
| Hao Fang | Scale-space exploration [PhD., 2016-2019] |
| Jean-Dominique Favreau | 3D modeling from sketches [PhD., 2014-2017, supervised with Adrien Bousseau] |
| Dorothy Duan | Semantized elevation maps [PhD., 2013-2016] |
| Sven Oesau | 3D Indoor reconstruction [PhD., 2012-2015, supervised with Pierre Alliez] |
| Yannick Verdie | Urban modeling from point clouds [PhD., 2010-2013] |
| Mulin Yu | pliant surface remeshing [Master, 2019, supervised with Pierre Alliez] |
| Alex Auvolat | Surface reconstruction by point processes [Master, 2018, supervised with Adrien Bousseau] |
| Leihan Chen | Floor maps vectorization [Master, 2017] |
| Hao Fang | Multiscale Primitive detection [Master, 2015] |
| Jean-Dominique Favreau | 3D modeling from sketches [Master, 2014, supervised with Adrien Bousseau] |
| Paul Seron | Primitive-driven mesh approximation [Master, 2011, supervised with Pierre Alliez] |
| Ioan Dragan | Parallelization of Markov Random Field [Master 2010-2011] |
| Parmeet Bhatia | Urban library of 3D unitary elements [Master, 2010] |
| Marouene Amri | Classification of Lidar data [Master, 2010] |
| Yann Bhogal | Survey on roof typology [Master, 2006] |
| Pierre Trontin | Building footprint vectorization [Master, 2005] |
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Organization of scientific events |
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