Delaunay Deformable Models: Topology-Adaptive Meshes Based on the Restricted Delaunay Triangulation

International audienceIn this paper, we propose a robust and efficient La- grangian approach, which we call Delaunay Deformable Models, for modeling moving surfaces undergoing large de- formations and topology changes. Our work uses the con- cept of restricted Delaunay triangulation, borrowed from computational geometry. In our approach, the interface is represented by a triangular mesh embedded in the Delau- nay tetrahedralization of interface points. The mesh is it- eratively updated by computing the restricted Delaunay tri- angulation of the deformed objects. Our method has many advantages over popular Eulerian techniques such as the level set method and over hybrid Eulerian-Lagrangian tech- niques such as the particle level set method: localization accuracy, adaptive resolution, ability to track properties as- sociated to the interface, seamless handling of triple junc- tions. Our work brings a rigorous and efficient alternative to existing topology-adaptive mesh techniques such as T- snakes

3D reconstruction of large scale city models as a support to Sustainable Development

International audienceNo part of the economic community can now escape from the urgent issues related to global warming, carbon footprint and reducing energy consumption. Nevertheless, the construction sector is particularly under pressure. Indeed, it is one of the biggest consumers of energy. It also largely contributes to the massive use of some critical resources (such as energy, water, materials and space...) and is responsible for a large portion of greenhouse gas emissions. In that context, the paper explores new approaches for urban planning by combining Virtual Environments and Simulations to address sustainability issues. These approaches are based on the possibilities of reconstructing 3D models of the built environment using standard photographs taken with off-the shelf hand-held digital cameras. The 3D models can then be combined with simulations in order to address sustainable urban development issues

Photo-consistent surface reconstruction from noisy point clouds

International audienceExisting algorithms for surface reconstruction from point sets are defeated by moderate amounts of noise and outliers, which makes them unapplicable to point clouds originating from multi-view image data. In this paper, we present a novel method which incorporates the input images in the surface reconstruction process for a better accuracy and robustness. Our approach is based on the medial axis transform of the scene, which our algorithm estimates through a global photo-consistency optimization by simulated annealing. A faithful polyhedral representation of the scene is then obtained by inversion of the medial axis transform

Hierarchical shape-based surface reconstruction for dense multi-view stereo

International audienceThe recent widespread availability of urban imagery has lead to a growing demand for automatic modeling from multiple images. However, modern image-based modeling research has focused either on highly detailed reconstructions of mostly small objects or on human-assisted simplified modeling. This paper presents a novel algorithm which automatically outputs a simplified, segmented model of a scene from a set of calibrated input images, capturing its essential geometric features. Our approach combines three successive steps. First, a dense point cloud is created from sparse depth maps computed from the input images. Then, shapes are robustly extracted from this set of points. Finally, a compact model of the scene is built from a spatial subdivision induced by these structures: this model is a global minimum of an energy accounting for the visibility of the final surface. The effectiveness of our method is demonstrated through several results on both synthetic and real data sets, illustrating the various benefits of our algorithm, its robustness and its relevance for architectural scenes

Towards high-resolution large-scale multi-view stereo

International audienceBoosted by the Middlebury challenge, the precision of dense multi-view stereovision methods has increased drastically in the past few years. Yet, most methods, although they perform well on this benchmark, are still inapplicable to large-scale data sets taken under uncontrolled conditions. In this paper, we propose a multi-view stereo pipeline able to deal at the same time with very large scenes while still producing highly detailed reconstructions within very reasonable time. The keys to these benefits are twofold: (i) a minimum s-t cut based global optimization that transforms a dense point cloud into a visibility consistent mesh, followed by (ii) a mesh-based variational refinement that captures small details, smartly handling photo-consistency, regularization and adaptive resolution. Our method has been tested on numerous large-scale outdoor scenes. The accuracy of our reconstructions is also measured on the recent dense multi-view benchmark proposed by Strecha et al., showing our results to compare more than favorably with the current state-of-the-art

On the use of the Hotelling's T2 statistic for the hierarchical clustering of hyperspectral data

International audienceIn this work we propose a hierarchical clustering methodology for hyperspectral data based on the Hotelling's T2 statistic. For each hypespectral sample data, the statistical sample mean is calculated using a window-based neighborhood. Then, the pairwise similarities between any two hyperspectral samples are computed based on the Hotelling's T2 statistic. This statistic assumes a Gaussian distribution of the data while hyperspectral data have been observed to be long tailed distributed. In order to improve the statistic robustness we use the Fixed Point estimates, and compare them to the classical sample mean estimator. The similarities are then used to hierarchically cluster the hyperspectral data. We give some preliminary qualitative results of the proposed approach over the Indian Pines hyperspectral scene. Results show that the use of the Fixed Point estimator does not significantly affect the clustering results. Further work will be focused on the use of the robust Hotelling statistic

Stéréo multi-vues : erreur de reprojection et maillages triangulaires

National audienceCet article propose une méthode variationnelle basée sur les maillages pour la reconstruction 3D de scènes (forme et radiance) à partir de plusieurs images. Notre méthode est basée sur les modèles génératifs et minimise l'erreur de reprojection (différence entre une image observée et une image obtenue à partir de la reconstruction) par une descente de gradient. Pour la première fois, nous calculons le gradient de l'erreur de reprojection pour des surfaces non lisses représentées de manière discrète par des maillages triangulés. Le gradient prend correctement en compte les changements de visibilité qui apparaissent lorsque la surface bouge durant l'évolution ; cela force les contours occultants générés par la surface à correspondre parfaitement aux contours apparents dans les images observées. Notre méthode est capable de retrouver la forme et la radiance de diverses scènes

Binary partition trees-based robust adaptive hyperspectral RX anomaly detection

International audienceThe Reed-Xiaoli (RX) is considered as the benchmark algorithm in multidimensional anomaly detection (AD). However, the RX detector performance decreases when the statistical parameters estimation is poor. This could happen when the background is non-homogeneous or the noise independence assumption is not fulfilled. For a better performance, the statistical parameters are estimated locally using a sliding window approach. In this approach, called adaptive RX, a window is centered over the pixel under the test (PUT), so the background mean and covariance statistics are estimated us- ing the data samples lying inside the window's spatial support, named the secondary data. Sometimes, a smaller guard window prevents those pixels close to the PUT to be used, in order to avoid the presence of outliers in the statistical estimation. The size of the window is chosen large enough to ensure the invertibility of the covariance matrix and small enough to justify both spatial and spectral homogeneity. We present here an alternative methodology to select the secondary data for a PUT by means of a binary partition tree (BPT) representation of the image. We test the proposed BPT-based adaptive hyperspectral RX AD algorithm using a real dataset provided by the Target Detection Blind Test project

Minimizing the Multi-view Stereo Reprojection Error for Triangular Surface Meshes

International audienceThis article proposes a variational multi-view stereo vision method based on meshes for recovering 3D scenes (shape and radiance) from images. Our method is based on generative models and minimizes the reprojection error (difference between the observed images and the images synthesized from the reconstruction). Our contributions are twofold. 1) For the first time, we rigorously compute the gradient of the reprojection error for non smooth surfaces defined by discrete triangular meshes. The gradient correctly takes into account the visibility changes that occur when a surface moves; this forces the contours generated by the reconstructed surface to perfectly match with the apparent contours in the input images. 2) We propose an original modification of the Lambertian model to take into account deviations from the constant brightness assumption without explicitly modelling the reflectance properties of the scene or other photometric phenomena involved by the camera model. Our method is thus able to recover the shape and the diffuse radiance of non Lambertian scenes