ALLIEZ , Pierre

International audience

This special issue of Graphical Models incorporates a selection of papers presented at the recent International Conference on Urban Physics held in Quito - Galápagos, 25 September–2 October 2016, Ecuador. The conference was intended to provide a major international forum for exchanging research ideas and significant practical results related to...

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...

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...

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When dealing with real-time simulation, radiative thermal computations have always been, and are still a challenge. Notably, computing view factors is very compute-intensive when the input 3D model is complex and exhibits many holes and occlusions. The task is even more difficult on complex geometries generated through topological optimization...

n dense labeling problem, the major drawback of the convolutional neural networks is their inability to learn new classes without affecting performance for the old classes on the data, having no annotations for the previous classes. In this work, we address the issue of adding new classes continually to the already trained network from a stream...

Radiative heat transfer or light transport are primarily governed by geometric view factors between surface elements. For general surfaces, calculating accurate geometric view factors requires solving integrals via quadrature methods. For complex scenes with many objects and obstacles such calculations are compute-intensive, preventing...

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...

Presentation at the European Space Thermal Engineering Workshop 2020 (slides)

Classifying 3D measurement data has become a core problem in photogram-metry and 3D computer vision, since the rise of modern multiview geometry techniques, combined with affordable range sensors. We introduce a Markov Random Field-based approach for segmenting textured meshes generated via multi-view stereo into urban classes of interest. The...

We introduce a novel approach that reconstructs 3D urban scenes in the form of levels of detail (LODs). Starting from raw data sets such as surface meshes generated by multi-view stereo systems, our algorithm proceeds in three main steps: classification, abstraction and reconstruction. From geometric attributes and a set of semantic rules...

International audience

We present a novel approach for the decimation of trian-gle surface meshes. Our algorithm takes as input a triangle surface mesh and a set of planar proxies detected in a pre-processing analysis step, and structured via an adjacency graph. It then performs greedy mesh decimation through a series of edge collapse, designed to approximate the...

In spite of remarkable success of the convolutional neural networks on semantic segmentation, they suffer from catastrophic forgetting: a significant performance drop for the already learned classes when new classes are added on the data, having no annotations for the old classes. We propose an incre-mental learning methodology, enabling to...

International audience

Radial-based 3D watermarking alters the distances between the center of mass of the 3D mesh and its vertices. These watermarking systems are inherently sensitive to cropping. To address this limitation, this paper introduces a complementary blind resynchronization module to transmit critical synchronization information to the watermark decoder....

We introduce in this paper an algorithm that generates from an input tolerance volume a surface triangle mesh guaranteed to be within the tolerance, intersection free and topologically correct. A pliant meshing algorithm is used to capture the topology and discover the anisotropy in the input tolerance volume in order to generate a concise...

This paper details the first publicly available implementation of the progressive mesh compression algorithm described in the paper entitled "Compressed Progressive Meshes" [R. Pajarola and J. Rossignac, IEEE Transactions on Visualization and Computer Graphics, 6 (2000), pp. 79-93]. Our implementation is generic, modular, and includes several...

One of the most popular and challenging tasks in remote sensing applications is the generation of digitized representations of Earth's objects from satellite raster image data. A common approach to tackle this challenge is a two-step method that first involves performing a pixel-wise classification of the raster data, then vectorizing the...

CAD models represented by NURBS surface patches are often hampered with defects due to inaccurate representations of trimming curves. Such defects make these models unsuitable to the direct generation of valid volume meshes, and often require trial-and-error processes to fix them. We propose a fully automated Delaunay-based meshing approach...

While initially devised for image categorization, convolutional neural networks (CNNs) are being increasingly used for the pixelwise semantic labeling of images. However, the proper nature of the most common CNN architectures makes them good at recognizing but poor at localizing objects precisely. This problem is magnified in the context of...

Although convolutional neural networks have been proven to be an effective tool to generate high quality maps from remote sensing images, their performance significantly deteriorates when there exists a large domain shift between training and test data. To address this issue, we propose a new data augmentation approach that transfers the style...

Due to the various reasons such as atmospheric effects and differences in acquisition, it is often the case that there exists a large difference between spectral bands of satellite images collected from different geographic locations. The large shift between spectral distributions of training and test data causes the current state of the art...

Convolutional neural networks (CNNs) have received increasing attention over the last few years. They were initially conceived for image categorization, i.e., the problem of assigning a semantic label to an entire input image.In this paper we address the problem of dense semantic labeling, which consists in assigning a semantic label to every...