Research activities


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.

Spatial point process

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:

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]

City modeling

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]

Roof skeletization [JPRS08]

Geometric shape detection

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]

Surface reconstruction

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]

Surface approximation

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]

Mesh simplification by hybrid surfaces [BMVC09, IP10]

Object and scene classification

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]

Sketch interpretation

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]


Student supervision
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]
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]


Organization of scientific events
ISPRS Congress in Nice (2020)
Tutorial at ECCV on Computational Geometry Tools and Applications in Computer Vision (2016)

CVPR workshop on Point Cloud Processing (2012)