LAFARGE , Florent

This thesis tackles the problem of the 3D building reconstruction from very high resolution satellite images. The information provided by this kind of data are not accurate enough to allow an efficient use of the varied algorithms developped in an aerial framework. In this context, it is necessary to introduce strong prior knowledge related to...

In this paper we present an update on the geometric modeling of urban scenes from physical measurements. This field of research has been studied for more than thirty years, but remains an important challenge in many scientific communi-ties as photogrammetry, computer vision, robotics or computer graphics. After introducing the objectives and...

Automatic city modeling from satellite imagery is one of the biggest challenges in urban reconstruction. Existing methods produce at best rough and dense Digital Surface Models. Inspired by recent works on semantic 3D reconstruction and region-based stereovision, we propose a method for producing compact , semantic-aware and geometrically...

Traditional Convolutional Neural Networks (CNN) for semantic segmentation of images use 2D convolution operations. While the spatial inductive bias of 2D convolutions allow CNNs to build hierarchical feature representations, they require that the whole feature maps are kept in memory until the end of the inference. This is not ideal for memory...

Analyzing and extracting geometric features from 3D data is a fundamental step in 3D scene understanding. Recent works demonstrated that deep learning archi-tectures can operate directly on raw point clouds, i.e. without the use of intermediate grid-like structures. These architectures are however not designed to encode contextual information...

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We present a global registration algorithm for multi-modal geometric data, typically 3D point clouds and meshes. Existing feature-based methods and recent deep learning based approaches typically rely upon point-to-point matching strategies that often fail to deliver accurate results from defect-laden data. In contrast, we reason at the scale...

We present a robust method for modeling cities from 3D-point data. Our algorithm provides a more complete description than existing approaches by reconstructing simultaneously buildings, trees and topographically complex grounds. A major contribution of our work is the original way of modeling buildings which guarantees a high generalization...

Converting point clouds generated by Laser scanning, multiview stereo imagery or depth cameras into compact polygon meshes is a challenging problem in vision. Existing methods are either robust to imperfect data or scalable, but rarely both. In this paper, we address this issue with an hybrid method that successively connects and slices planes...

We present an algorithm for detecting planar primitives from unorganized 3D point clouds. Departing from an initial configuration, the algorithm refines both the continuous plane parameters and the discrete assignment of input points to them by seeking high fidelity, high simplicity and high completeness. Our key contribution relies upon the...

The over-segmentation of images into atomic regions has become a standard and powerful tool in Vision. Traditional superpixel methods, that operate at the pixel level, cannot directly capture the geometric information disseminated into the images. We propose an alternative to these methods by operating at the level of geometric shapes. Our...

Plane arrangements are a useful tool for surface and volume modelling. However, their main drawback is poor scalability. We introduce two key novelties that enable the construction of plane arrangements for complex objects and entire scenes: (i) an ordering scheme for the plane insertion and (ii) the direct use of input points during...

Recent works showed that floating polygons can be an interesting alternative to traditional superpixels, especially for analyzing scenes with strong geometric signatures , as man-made environments. Existing algorithms produce homogeneously-sized polygons that fail to capture thin geometric structures and over-partition large uniform areas. We...

Converting point clouds into concise polygonal meshes in an automated manner is an enduring problem in Computer Graphics. Prior work, which typically operate by assembling planar shapes detected from input points, largely overlooked the scalability issue of processing a large number of shapes. As a result, they tend to produce overly simplified...

We introduce a pipeline that reconstructs buildings of urban environments as concise polygonal meshes from airborne LiDAR scans.It consists of three main steps : classification, building contouring, and building reconstruction, the two last steps being achievedusing computational geometry tools. Our algorithm demonstrates its robustness,...

We present a method for planar shape detection and regularization from raw point sets. The geometric modeling and processing of man-made environments from measurement data often relies upon robust detection of planar primitive shapes. In addition, the detection and reinforcement of regularities between planar parts is a means to increase...

Automatic road graph extraction from aerial and satellite images is a long-standing challenge. Existing algorithms are either based on pixel-level segmentation followed by vectorization, or on iterative graph construction using next move prediction. Both of these strategies suffer from severe drawbacks, in particular high computing resources...

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 distiction between our class of interest and the other classes :...

We present a supervised machine learning approach for classification of objects from sampled point data. The main idea consists in first abstracting the input object into planar parts at several scales, then discriminate between the different classes of objects solely through features derived from these planar shapes. Abstracting into planar...

The Thermical InfraRed (TIR) channel contains wave lengths sensitive to the emission of heat. The forest fires can be characterized by intensity peaks on TIR images. We present a fully automatic method of forest fi re detection from TIR satellite images based on the random fi eld theory. First, preprocessing is used to model the image as a...

Nous détaillons dans ce rapport la construction de deux noyaux texturaux s'utilisant dans les problèmes de classification par «Support Vector Machines» en télédétection. Les SVM constituent une méthode de classification supervisée particulièrement bien adaptée pour traiter des données de grande dimension telles que les images satellitaires. Par...

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...

Nous proposons, dans ce rapport, une méthode de détection des feux de forêt par imagerie satellitaire fondée sur la théorie des champs aléatoires. L'idée consiste à modéliser l'image par une réalisation d'un champ gaussien afin d'en extraire, par une analyse statistique, les éléments étrangers pouvant correspondre aux feux. Le canal IRT...

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...

While object detection methods traditionally make use of pixel-level masks or bounding boxes, alternative representations such as polygons or active contours have recently emerged. Among them, methods based on the regression of Fourier or Chebyshev coefficients have shown high potential on freeform objects. By defining object shapes as polar...