Interactive procedural simulation of paper tearing with sound

International audienceWe present a phenomenological model for the real-time simulation of paper tearing and sound. The model uses as input rotations of the hand along with the index and thumb of left and right hands to drive the position and orientation of two regions of a sheet of paper. The motion of the hands produces a cone shaped deformation of the paper and guides the formation and growth of the tear. We create a model for the direction of the tear based on empirical observation, and add detail to the tear with a directed noise model. Furthermore, we present a procedural sound synthesis method to produce tearing sounds during interaction. We show a variety of paper tearing examples and discuss applications and limitations

Analyse multi-échelle de nuage de points

3D acquisition techniques like photogrammetry and laser scanning are commonly used in numerous fields such as reverse engineering, archeology, robotics and urban planning. The main objective is to get virtual versions of real objects in order to visualize, analyze and process them easily. Acquisition techniques become more and more powerful and affordable which creates important needs to process efficiently the resulting various and massive 3D data. Data are usually obtained in the form of unstructured 3D point cloud sampling the scanned surface. Traditional signal processing methods cannot be directly applied due to the lack of spatial parametrization. Points are only represented by their 3D coordinates without any particular order. This thesis focuses on the notion of scale of analysis defined by the size of the neighborhood used to locally characterize the point-sampled surface. The analysis at different scales enables to consider various shapes which increases the analysis pertinence and the robustness to acquired data imperfections. We first present some theoretical and practical results on curvature estimation adapted to a multi-scale and multi-resolution representation of point clouds. They are used to develop multi-scale algorithms for the recognition of planar and anisotropic shapes such as cylinders and feature curves. Finally, we propose to compute a global 2D parametrization of the underlying surface directly from the 3D unstructured point cloud.Les techniques d'acquisition numérique 3D comme la photogrammétrie ou les scanners laser sont couramment utilisées dans de nombreux domaines d'applications tels que l'ingénierie inverse, l'archéologie, la robotique, ou l'urbanisme. Le principal objectif est d'obtenir des versions virtuels d'objets réels afin de les visualiser, analyser et traiter plus facilement. Ces techniques d'acquisition deviennent de plus en plus performantes et accessibles, créant un besoin important de traitement efficace des données 3D variées et massives qui en résultent. Les données sont souvent obtenues sont sous la forme de nuage de points 3D non-structurés qui échantillonnent la surface scannée. Les méthodes traditionnelles de traitement du signal ne peuvent alors s'appliquer directement par manque de paramétrisation spatiale, les points étant explicités par leur coordonnées 3D, sans ordre particulier. Dans cette thèse nous nous focalisons sur la notion d'échelle d'analyse qui est définie par la taille du voisinage utilisé pour caractériser localement la surface échantillonnée. L'analyse à différentes échelles permet de considérer des formes variées et ainsi rendre l'analyse plus pertinente et plus robuste aux imperfections des données acquises. Nous présentons d'abord des résultats théoriques et pratiques sur l'estimation de courbure adaptée à une représentation multi-échelle et multi-résolution de nuage de points. Nous les utilisons pour développer des algorithmes multi-échelle de reconnaissance de formes planaires et anisotropes comme les cylindres et les lignes caractéristiques. Enfin, nous proposons de calculer une paramétrisation 2D globale de la surface sous-jacente directement à partir de son nuage de points 3D non-structurés

Multi-scale Point Cloud Analysis

Les techniques d'acquisition numérique 3D comme la photogrammétrie ou les scanners laser sont couramment utilisées dans de nombreux domaines d'applications tels que l'ingénierie inverse, l'archéologie, la robotique, ou l'urbanisme. Le principal objectif est d'obtenir des versions virtuels d'objets réels afin de les visualiser, analyser et traiter plus facilement. Ces techniques d'acquisition deviennent de plus en plus performantes et accessibles, créant un besoin important de traitement efficace des données 3D variées et massives qui en résultent. Les données sont souvent obtenues sont sous la forme de nuage de points 3D non-structurés qui échantillonnent la surface scannée. Les méthodes traditionnelles de traitement du signal ne peuvent alors s'appliquer directement par manque de paramétrisation spatiale, les points étant explicités par leur coordonnées 3D, sans ordre particulier. Dans cette thèse nous nous focalisons sur la notion d'échelle d'analyse qui est définie par la taille du voisinage utilisé pour caractériser localement la surface échantillonnée. L'analyse à différentes échelles permet de considérer des formes variées et ainsi rendre l'analyse plus pertinente et plus robuste aux imperfections des données acquises. Nous présentons d'abord des résultats théoriques et pratiques sur l'estimation de courbure adaptée à une représentation multi-échelle et multi-résolution de nuage de points. Nous les utilisons pour développer des algorithmes multi-échelle de reconnaissance de formes planaires et anisotropes comme les cylindres et les lignes caractéristiques. Enfin, nous proposons de calculer une paramétrisation 2D globale de la surface sous-jacente directement à partir de son nuage de points 3D non-structurés.3D acquisition techniques like photogrammetry and laser scanning are commonly used in numerous fields such as reverse engineering, archeology, robotics and urban planning. The main objective is to get virtual versions of real objects in order to visualize, analyze and process them easily. Acquisition techniques become more and more powerful and affordable which creates important needs to process efficiently the resulting various and massive 3D data. Data are usually obtained in the form of unstructured 3D point cloud sampling the scanned surface. Traditional signal processing methods cannot be directly applied due to the lack of spatial parametrization. Points are only represented by their 3D coordinates without any particular order. This thesis focuses on the notion of scale of analysis defined by the size of the neighborhood used to locally characterize the point-sampled surface. The analysis at different scales enables to consider various shapes which increases the analysis pertinence and the robustness to acquired data imperfections. We first present some theoretical and practical results on curvature estimation adapted to a multi-scale and multi-resolution representation of point clouds. They are used to develop multi-scale algorithms for the recognition of planar and anisotropic shapes such as cylinders and feature curves. Finally, we propose to compute a global 2D parametrization of the underlying surface directly from the 3D unstructured point cloud

Persistence Analysis of Multi-scale Planar Structure Graph in Point Clouds

International audienceModern acquisition techniques generate detailed point clouds that sample complex geometries. For instance, we are able to produce millimeter-scale acquisition of whole buildings. Processing and exploring geometrical information within such point clouds requires scalability, robustness to acquisition defects and the ability to model shapes at different scales. In this work, we propose a new representation that enriches point clouds with a multi-scale planar structure graph. We define the graph nodes as regions computed with planar segmentations at increasing scales and the graph edges connect regions that are similar across scales. Connected components of the graph define the planar structures present in the point cloud within a scale interval. For instance, with this information, any point is associated to one or several planar structures existing at different scales. We then use topological data analysis to filter the graph and provide the most prominent planar structures. Our representation naturally encodes a large range of information. We show how to efficiently extract geometrical details (e.g. tiles of a roof), arrangements of simple shapes (e.g. steps and mean ramp of a staircase), and large-scale planar proxies (e.g. walls of a building) and present several interactive tools to visualize, select and reconstruct planar primitives directly from raw point clouds. The effectiveness of our approach is demonstrated by an extensive evaluation on a variety of input data, as well as by comparing against state-of-the-art techniques and by showing applications to polygonal mesh reconstruction

Multi-scale Planar Segments Extraction from Point Clouds

International audienceWe propose a flexible method to extract a set of segments from a 3D point cloud that are relevant across several scales and optimal in a planar sense. Since planar geometric primitives are ubiquitous, especially in man-made scene, their accurate detection is crucial for an abstract representation of point-based 3D data. In this paper, we introduce a new hierarchical graph representation in which each node represents a region at a given scale. The proposed graph is initialized with multiple segmentations performed at different scales and then reduced by collapsing groups of nodes. Each resulting group of nodes defines a meaningful segment and is obtained through an optimization that balances number of extracted segments and accuracy with respect to the input data in a planar sense. The output graph is a compact abstraction of the input cloud into multiple, possibly overlapping, segments, each relevant at a certain scale. The edges of the graph connect nodes whose segments overlap across different scales, thus allowing to represent both detailed and approximating parts of the scene

Stable and efficient differential estimators on oriented point clouds

International audiencePoint clouds are now ubiquitous in computer graphics and computer vision. Differential properties of the point-sampled surface, such as principal curvatures, are important to estimate in order to locally characterize the scanned shape. To approximate the surface from unstructured points equipped with normal vectors, we rely on the Algebraic Point Set Surfaces (APSS) [GG07] for which we provide convergence and stability proofs for the mean curvature estimator. Using an integral invariant viewpoint, this first contribution links the algebraic sphere regression involved in the APSS algorithm to several surface derivatives of different orders. As a second contribution, we propose an analytic method to compute the shape operator and its principal curvatures from the fitted algebraic sphere. We compare our method to the state-of-the-art with several convergence and robustness tests performed on a synthetic sampled surface. Experiments show that our curvature estimations are more accurate and stable while being faster to compute compared to previous methods. Our differential estimators are easy to implement with little memory footprint and only require a unique range neighbors query per estimation. Its highly parallelizable nature makes it appropriate for processing large acquired data, as we show in several real-world experiments

PCEDNet : A Lightweight Neural Network for Fast and Interactive Edge Detection in 3D Point Clouds

International audienceIn recent years, Convolutional Neural Networks (CNN) have proven to be efficient analysis tools for processing point clouds, e.g., for reconstruction, segmentation and classification. In this paper, we focus on the classification of edges in point clouds, where both edges and their surrounding are described. We propose a new parameterization adding to each point a set of differential information on its surrounding shape reconstructed at different scales. These parameters, stored in a Scale-Space Matrix (SSM), provide a well suited information from which an adequate neural network can learn the description of edges and use it to efficiently detect them in acquired point clouds. After successfully applying a multi-scale CNN on SSMs for the efficient classification of edges and their neighborhood, we propose a new lightweight neural network architecture outperforming the CNN in learning time, processing time and classification capabilities. Our architecture is compact, requires small learning sets, is very fast to train and classifies millions of points in seconds

SLAM-aided forest plot mapping combining terrestrial and mobile laser scanning

International audiencePrecise structural information collected from plots is significant in the management of and decision-making regarding forest resources. Currently, laser scanning is widely used in forestry inventories to acquire three-dimensional (3D) structural information. There are three main data-acquisition modes in ground-based forest measurements: single-scan terrestrial laser scanning (TLS), multi-scan TLS and multi-single-scan TLS. Nevertheless, each of these modes causes specific difficulties for forest measurements. Due to occlusion effects, the single-scan TLS mode provides scans for only one side of the tree. The multi-scan TLS mode overcomes occlusion problems, however, at the cost of longer acquisition times, more human labor and more effort in data preprocessing. The multi-single-scan TLS mode decreases the workload and occlusion effects but lacks the complete 3D reconstruction of forests. These problems in TLS methods are largely avoided with mobile laser scanning (MLS); however, the geometrical peculiarity of forests (e.g., similarity between tree shapes, placements, and occlusion) complicates the motion estimation and reduces mapping accuracy.Therefore, this paper proposes a novel method combining single-scan TLS and MLS for forest 3D data acquisition. We use single-scan TLS data as a reference, onto which we register MLS point clouds, so they fill in the omission of the single-scan TLS data. To register MLS point clouds on the reference, we extract virtual feature points that are sampling the centerlines of tree stems and propose a new optimization-based registration framework. In contrast to previous MLS-based studies, the proposed method sufficiently exploits the natural geometric characteristics of trees. We demonstrate the effectiveness, robustness, and accuracy of the proposed method on three datasets, from which we extract structural information. The experimental results show that the omission of tree stem data caused by one scan can be compensated for by the MLS data, and the time of the field measurement is much less than that of the multi-scan TLS mode. In addition, single-scan TLS data provide strong global constraints for MLS-based forest mapping, which allows low mapping errors to be achieved, e.g., less than 2.0 cm mean errors in both the horizontal and vertical directions

SHREC'19 track: Feature Curve Extraction on Triangle Meshes

International audienceThis paper presents the results of the SHREC'19 track: Feature curve extraction on triangle meshes. Given a model, the challenge consists in automatically extracting a subset of the mesh vertices that jointly represent a feature curve. As an optional task, participants were requested to send also a similarity evaluation among the feature curves extracted. The various approaches presented by the participants are discussed, together with their results. The proposed methods highlight different points of view of the problem of feature curve extraction. It is interesting to see that it is possible to deal with this problem with good results, despite the different approaches

SHREC'18 track: Recognition of geometric patterns over 3D models

International audienceThis track of the SHREC 2018 originally aimed at recognizing relief patterns over a set of triangle meshes from laser scan acquisitions of archaeological fragments. This track approaches a lively and very challenging problem that remains open after the end of the track. In this report we discuss the challenges to face to successfully address geometric pattern recognition over surfaces; how the existing techniques can go further in this direction, what is currently missing and what is necessary to be further developed