Florent Lafarge

University of Nice-Sophia Antipolis, Nice, Provence-Alpes-Côte d'Azur, France

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Publications (25)26.04 Total impact

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    ABSTRACT: We present an original multi-view stereo reconstruction algorithm which allows the 3D-modeling of urban scenes as a combination of meshes and geometric primitives. The method provides a compact model while preserving details: irregular elements such as statues and ornaments are described by meshes whereas regular structures such as columns and walls are described by primitives (planes, spheres, cylinders, cones and tori). We adopt a two-step strategy consisting first in segmenting the initial mesh-based surface using a multi-label Markov Random Field based model and second, in sampling primitive and mesh components simultaneously on the obtained partition by a Jump-Diffusion process. The quality of a reconstruction is measured by a multi-object energy model which takes into account both photo-consistency and semantic considerations (i.e. geometry and shape layout). The segmentation and sampling steps are embedded into an iterative refinement procedure which provides an increasingly accurate hybrid representation. Experimental results on complex urban structures and large scenes are presented and compared to the state-of-the-art multi-view stereo meshing algorithms.
    IEEE Transactions on Software Engineering 04/2012; · 2.59 Impact Factor
  • Florent Lafarge, Clément Mallet
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    ABSTRACT: We present a robust method for modeling cities from unstructured point data. Our algorithm provides a more complete description than existing approaches by reconstructing simultaneously buildings, trees and topologically complex grounds. Buildings are modeled by an original approach which guarantees a high generalization level while having semantized and compact representations. Geometric 3D-primitives such as planes, cylinders, spheres or cones describe regular roof sections, and are combined with mesh-patches that represent irregular roof components. The various urban components interact through a non-convex energy minimization problem in which they are propagated under arrangement constraints over a planimetric map. We experimentally validate the approach on complex urban structures and large urban scenes of millions of points.
    IEEE International Conference on Computer Vision, ICCV 2011, Barcelona, Spain, November 6-13, 2011; 01/2011
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    Yannick Verdie, Florent Lafarge, Josiane Zerubia
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    ABSTRACT: We present an automatic approach for modeling buildings from aerial LiDAR data. The method produces accurate, watertight and compact meshes under planar constraints which are especially designed for urban scenes. The LiDAR point cloud is classified through a non-convex energy minimization problem in order to separate the points labeled as building. Roof structures are then extracted from this point subset, and used to control the meshing procedure. Experiments highlight the potential of our method in term of minimal rendering, accuracy and compactness.
    18th IEEE International Conference on Image Processing, ICIP 2011, Brussels, Belgium, September 11-14, 2011; 01/2011
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    ABSTRACT: Lidar waveforms are 1-D signals representing a train of echoes caused by reflections at different targets. Modeling these echoes with the appropriate parametric function is useful to retrieve information about the physical characteristics of the targets. This paper presents a new probabilistic model based upon a marked point process which reconstructs the echoes from recorded discrete waveforms as a sequence of parametric curves. Such an approach allows to fit each mode of a waveform with the most suitable function and to deal with both, symmetric and asymmetric, echoes. The model takes into account a data term, which measures the coherence between the models and the waveforms, and a regularization term, which introduces prior knowledge on the reconstructed signal. The exploration of the associated configuration space is performed by a reversible jump Markov chain Monte Carlo (RJMCMC) sampler coupled with simulated annealing. Experiments with different kinds of lidar signals, especially from urban scenes, show the high potential of the proposed approach. To further demonstrate the advantages of the suggested method, actual laser scans are classified and the results are reported.
    IEEE Transactions on Image Processing 12/2010; 19(12):3204-21. · 3.20 Impact Factor
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    ABSTRACT: This paper presents a new stochastic marked point process for describing images in terms of a finite library of geometric objects. Image analysis based on conventional marked point processes has already produced convincing results but at the expense of parameter tuning, computing time, and model specificity. Our more general multimarked point process has simpler parametric setting, yields notably shorter computing times, and can be applied to a variety of applications. Both linear and areal primitives extracted from a library of geometric objects are matched to a given image using a probabilistic Gibbs model, and a Jump-Diffusion process is performed to search for the optimal object configuration. Experiments with remotely sensed images and natural textures show that the proposed approach has good potential. We conclude with a discussion about the insertion of more complex object interactions in the model by studying the compromise between model complexity and efficiency.
    IEEE Transactions on Software Engineering 09/2010; 32(9):1597-609. · 2.59 Impact Factor
  • F. Lafarge, R. Keriven, M. Brédif
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    ABSTRACT: We propose an original hybrid modeling process of urban scenes that represents 3-D models as a combination of mesh-based surfaces and geometric 3-D-primitives. Meshes describe details such as ornaments and statues, whereas 3-D-primitives code for regular shapes such as walls and columns. Starting from an 3-D-surface obtained by multiview stereo techniques, these primitives are inserted into the surface after being detected. This strategy allows the introduction of semantic knowledge, the simplification of the modeling, and even correction of errors generated by the acquisition process. We design a hierarchical approach exploring different scales of an observed scene. Each level consists first in segmenting the surface using a multilabel energy model optimized by -expansion and then in fitting 3-D-primitives such as planes, cylinders or tori on the obtained partition where relevant. Experiments on real meshes, depth maps and synthetic surfaces show good potential for the proposed approach.
    IEEE Transactions on Image Processing 08/2010; · 3.20 Impact Factor
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    ABSTRACT: We present a new approach for building reconstruction from a single Digital Surface Model (DSM). It treats buildings as an assemblage of simple urban structures extracted from a library of 3D parametric blocks (like a LEGO set). First, the 2D-supports of the urban structures are extracted either interactively or automatically. Then, 3D-blocks are placed on the 2D-supports using a Gibbs model which controls both the block assemblage and the fitting to data. A Bayesian decision finds the optimal configuration of 3D-blocks using a Markov Chain Monte Carlo sampler associated with original proposition kernels. This method has been validated on multiple data set in a wide-resolution interval such as 0.7 m satellite and 0.1 m aerial DSMs, and provides 3D representations on complex buildings and dense urban areas with various levels of detail.
    IEEE Transactions on Software Engineering 01/2010; 32(1):135-47. · 2.59 Impact Factor
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    ABSTRACT: We propose a multi-view stereo reconstruction algorithm which recovers urban scenes as a combination of meshes and geometric primitives. It provides a compact model while preserving details: irregular elements such as statues and ornaments are described by meshes whereas regular structures such as columns and walls are described by primitives (planes, spheres, cylinders, cones and tori). A Jump-Diffusion process is designed to sample these two types of elements simultaneously. The quality of a reconstruction is measured by a multi-object energy model which takes into account both photo-consistency and semantic considerations (i.e. geometry and shape layout). The sampler is embedded into an iterative refinement procedure which provides an increasingly accurate hybrid representation. Experimental results on complex urban structures and large scenes are presented and compared to multi-view based meshing algorithms.
    The Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2010, San Francisco, CA, USA, 13-18 June 2010; 01/2010
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    ABSTRACT: Lidar waveforms are 1D signal consisting of a train of echoes where each of them correspond to a scattering target of the Earth surface. Modeling these echoes with the appropriate parametric function is necessary to retrieve physical information about these objects and characterize their properties. This paper presents a marked point process based model to reconstruct a lidar signal in terms of a set of parametric functions. The model takes into account both a data term which measures the coherence between the models and the waveforms, and a regularizing term which introduces physical knowledge on the reconstructed signal. We search for the best configuration of functions by performing a Reversible Jump Markov Chain Monte Carlo sampler coupled with a simulated annealing. Results are finally presented on different kinds of signals in urban areas.
    Image Processing (ICIP), 2009 16th IEEE International Conference on; 12/2009
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    Florent Lafarge, Renaud Keriven, Mathieu Brédif
    British Machine Vision Conference, BMVC 2009, London, UK, September 7-10, 2009. Proceedings; 01/2009
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    Florent Lafarge, Georgy L. Gimel'farb
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    ABSTRACT: Our goal is to represent images in terms of geometric objects acting as prim- itive elements of an image description. Similar representations obtained by stochastic marked point processes have already led to convincing image anal- ysis results but suffer from serious drawbacks such as complex and unstable parameter tuning, large computing time, and lack of generality. We propose an alternative descriptive model based on a Jump-Diffusion process which can be performed in shorter computing times and applied to a variety of ap- plications without changing the model or modifying the tuning parameters. In our approach, a probabilistic Gibbs model is adapted to a library of geo- metric objects and is sampled by a Jump-Diffusion process in order to closely match an underlying texture. Experiments with natural textures and remotely sensed images show good potentialities of the proposed approach1.
    Proceedings of the British Machine Vision Conference 2008, Leeds, September 2008; 01/2008
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    ABSTRACT: This paper presents a new approach to describe images in terms of geometric objects. Methods based on conventional stochastic marked point processes have already led to convincing image analysis results but possess several drawbacks such as complex parameter tuning, large computing time, and lack of generality. We propose a generalized marked point process model which can be performed in shorter computing times and applied to a variety of applications without modifying the model or tuning parameters. In our approach, both linear and areal primitives extracted from a library of geometric objects are matched to a given image using a probabilistic Gibbs model. A Jump-Diffusion process is performed to find the optimal object configuration. Experiments with remotely sensed images show good potentialities of the proposed approach.
    Advanced Concepts for Intelligent Vision Systems, 10th International Conference, ACIVS 2008, Juan-les-Pins, France, October 20-24, 2008. Proceedings; 01/2008
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    ABSTRACT: We present a new approach for building reconstruction from a single Digital Elevation Model (DEM). It treats buildings as an assemblage of simple urban structures ex- tracted from a library of 3D parametric blocks (like a LEGO R � set). This method works on various data resolu- tions such as 0.7 m satellite and 0.1 m aerial DEMs and allows us to obtain 3D representations with various levels of detail. First, the 2D supports of the urban structures are extracted either interactively or automatically. Then, 3D blocks are placed on the 2D supports using a Gibbs model. A Bayesian decision finds the optimal configuration of 3D blocks using a RJMCMC sampler. Experimental results on complex buildings and dense urban areas are presented us- ing data at various resolutions1.
    2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2008), 24-26 June 2008, Anchorage, Alaska, USA; 01/2008
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    ABSTRACT: In this paper, we present an automatic building extraction method from Digital Elevation Models based on an object approach. First, a rough approximation of the building footprints is realized by a method based on marked point processes: the building footprints are modeled by rectangle layouts. Then, these rectangular footprints are regularized by improving the connection between the neighboring rectangles and detecting the roof height discontinuities. The obtained building footprints are structured footprints: each element represents a specific part of an urban structure. Results are finally applied to a 3D-city modeling process.
    ISPRS Journal of Photogrammetry and Remote Sensing. 01/2008;
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    ABSTRACT: We present an automatic 3D city model of dense urban areas from HR satellite data. The proposed method is developed using a structural approach: we construct complex buildings by merging simple parametric models with rectangular ground footprint. To do so, an automatic building extraction method based on marked point processes is used to provide rectangular building footprints. A collection of 3D parametric models is defined in order to be fixed onto these building footprints. A Bayesian framework including both prior knowledge of models and their interactions, and a likelihood fitting them to the digital elevation model, is then used. A simulated annealing scheme allows to find the configuration which maximizes the posterior density of the Bayesian expression
    Image Processing, 2006 IEEE International Conference on; 11/2006
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    ABSTRACT: We propose a parametric model for automatic 3D reconstruction of urban areas from high resolution satellite data. An automatic building extraction method based on marked point processes is used to provide rectangular building footprints. Based on a parametric model with rectangular ground footprint, the proposed method is developed using a Bayesian approach : we search for the best configuration of parametric models with respect to both a priori knowledge of models and their interactions, and a likelihood which fits models to the DEM. A simulated annealing is used to find the configuration which maximizes the a posteriori density of the Bayesian expression
    Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on; 06/2006 · 4.63 Impact Factor
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    F. Lafarge, X. Descombes, J. Zerubia
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    ABSTRACT: We present a textural kernel for "support vector machines" classification applied to remote sensing problems. SVMs constitute a method of supervised classification well adapted to deal with data of high dimension, such as images. We introduce kernel functions in order to favor the distinction between our class of interest and the other classes: it gives information of similarity. In our case this similarity is based on radiometric and textural characteristics. One of the main difficulties is to elaborate textural parameters which are relevant and characterize as well as possible the joint distribution of a set of connected pixels. We apply this method to remote sensing problems: the detection of forest fires and the extraction of urban areas in high resolution images.
    Image Processing, 2005. ICIP 2005. IEEE International Conference on; 10/2005
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    ABSTRACT: Le canal IRT (InfraRouge Thermique) contient des longueurs d'onde particulièrement sensibles à l'émission de chaleur. Les feux de forêt sont alors caractérisés par des pics d'intensité sur ce type d'images. Nous proposons une méthode automatique de détection de feux de forêt fondée sur une analyse statistique des pics d'intensité de l'image. Pour ce faire, nous modélisons dans un premier temps l'image par une réalisation d'un champ gaussien, champ ayant des propriétés particulièrement intéressantes. Les zones de feux, minoritaires et de fortes intensités sont considérées comme des éléments étrangers à ce champ : ce sont des événements rares. Ensuite, par une analyse statistique, nous déterminons un jeu de probabilités définissant, pour une zone donnée de l'image, un degré d'appartenance au champ gaussien, et par complémentarité aux zones potentiellement en feux.
    20° Colloque sur le traitement du signal et des images, 2005 ; p. 17-20.
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    ABSTRACT: In this paper, we present an automatic method for the 3D building reconstruction from satellite images. The proposed approach consists in reconstructing buildings by assembling simple urban structures extracted from a grammar of 3D parametric models, as a "LEGO" game. First, the building footprints are extracted through sequences of quadrilaterals: it allows to define the problem as a causal process. Then, the 3D reconstruction stage is realized through a Hidden Markov Model and the optimal sequences of 3D parametric objects are found using the Viterbi algorithm.
    Image Processing, 2007. ICIP 2007. IEEE International Conference on;
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    ABSTRACT: The TIR (Thermical InfraRed) channel owns wave lengths sensitive to the emission of heat. So the higher the heat of an area, the higher the intensity of the corresponding pixel of the image. Then the forest fire can be caracterize by intensity peak on that kind of images. We present a fully automatic method of forest fire detection in satellite images based on the random field theory. First we model the image by a realization of a Gaussian field. The fire areas which have high intensity and are supposed to be a minority are considered as foreign elements of that field : they are rare events. Then we determine by a statistical analysis a set of probabilities which caracterizes a degree of belonging to the Gaussian field of a small area of the image. By complementarity, we estimate the probability that this area is a potential fire. Le canal IRT (InfraRouge Thermique) contient des longueurs d'onde particulièrement sensibles à l'émission de chaleur. Les feux de forêt peuvent alors être caractérisés par des pics d'intensité sur des images IRT. Nous proposons une méthode automatique de détection des feux de forêt par imagerie satellitaire fondée sur la théorie des champs aléatoires. Pour ce faire, nous cherchons à modéliser dans un premier temps l'image par une réalisation d'un champ gaussien. Les zones de feux, minoritaires et de fortes intensités sont considérées comme des éléments étrangers à ce champ : ce sont des évènements rares. Ensuite, par une analyse statistique, nous déterminons un jeu de probabilités définissant, pour une zone donnée de l'image, un degré d'appartenance au champ gaussien, et par complémentarité aux zones potentiellement en feux.