On Volumetric Shape Reconstruction from Implicit Forms

International audienceIn this paper we report on the evaluation of volumetric shape reconstruction methods that consider as input implicit forms in 3D. Many visual applications build implicit representations of shapes that are converted into explicit shape representations using geometric tools such as the Marching Cubes algorithm. This is the case with image based reconstructions that produce point clouds from which implicit functions are computed, with for instance a Poisson reconstruction approach. While the Marching Cubes method is a versatile solution with proven efficiency, alternative solutions exist with different and complementary properties that are of interest for shape modeling. In this paper, we propose a novel strategy that builds on Centroidal Voronoi Tessellations (CVTs). These tessellations provide volumetric and surface representations with strong regularities in addition to provably more accurate approximations of the implicit forms considered. In order to compare the existing strategies, we present an extensive evaluation that analyzes various properties of the main strategies for implicit to explicit volumetric conversions: Marching cubes, Delaunay refinement and CVTs, including accuracy and shape quality of the resulting shape mesh

A Hierarchical Approach for Regular Centroidal Voronoi Tessellations

International audienceIn this paper we consider Centroidal Voronoi Tessellations (CVTs) and study their regularity. CVTs are geometric structures that enable regular tessellations of geometric objects and are widely used in shape modeling and analysis. While several efficient iterative schemes, with defined local convergence properties, have been proposed to compute CVTs, little attention has been paid to the evaluation of the resulting cell decompositions. In this paper, we propose a regularity criterion that allows us to evaluate and compare CVTs independently of their sizes and of their cell numbers. This criterion allows us to compare CVTs on a common basis. It builds on earlier theoretical work showing that second moments of cells converge to a lower bound when optimising CVTs. In addition to proposing a regularity criterion, this paper also considers computational strategies to determine regular CVTs. We introduce a hierarchical framework that propagates regularity over decomposition levels and hence provides CVTs with provably better regularities than existing methods. We illustrate these principles with a wide range of experiments on synthetic and real models

État de l'art des méthodes de segmentation de séquences de maillages et proposition d'une classification

National audienceLes séquences de maillages représentent un nouveau type de contenu, de plus en plus utilisé dans le domaine du multimédia. Les applications que sont la compression, l'indexation, etc. s'appliquent désormais à ce type de données. Compte-tenu de la taille très importante de ces données, une segmentation préalable est souvent nécessaire. Dans cet article, nous proposons dans un premier temps une formalisation des notions de "séquence de maillages" et de "segmentation de séquence de maillages". Puis nous présentons un état de l'art des différentes méthodes de segmentation de séquences de maillages. Finalement, nous présentons différentes applications possibles de la segmentation de séquences de maillages

Propriétés topologiques pour la modélisation géométrique de domaines d'études comportant des singularités non-variétés

L’étude de comportement mécanique de structures et/ou d’écoulements s’appuie fréquemment sur des modèles géométriques perçus comme des assemblages de volumes, surfaces, lignes, connectés entre eux et comportant des singularités non-variétés. Une classification d’objets comportant des singularités non-variétés et des propriétés topologiques globales sont présentées pour accroître l’efficacité des modeleurs et la génération des contraintes de maillages

Segmentation of tree seedling point clouds into elementary units

International audienceThis paper describes a new semi-automatic method to cluster TLS data into meaningful sets of points to extract plant components. The approach is designed for small plants with distinguishable branches and leaves, such as tree seedlings. It first creates a graph by connecting each point to its most relevant neighbours, then embeds the graph into a spectral space, and finally segments the embedding into clusters of points. The process can then be iterated on each cluster separately. The main idea underlying the approach is that the spectral embedding of the graph aligns the points along the shape's principal directions. A quantitative evaluation of the segmentation accuracy, as well as of leaf area estimates, is provided on a poplar seedling mock-up. It shows that the segmentation is robust with false positive and false negative rates around 1%. Qualitative results on four contrasting plant species with three different scan resolution levels each are also shown

Atlas-Based Character Skinning with Automatic Mesh Decomposition

Skinning is the most tedious part in the character animation process. Using standard methods, joint weights must be attached to each vertex of the character's mesh, which is often time-consuming if an accurate animation is required. We propose a new modeling of the skinning process, inspired by the notion of atlas of charts. Starting from the character's animation skeleton, we first automatically decompose the mesh into anatomically meaningful overlapping regions. Regions are then blended in their overlapping parts using continuous transition functions. This leads to a simple yet efficient skinning process for which the weights are automatically defined and do not depend on the Euclidean distance but on the distance on the surface.Le skinning est l'étape la plus fastidieuse du processus d'animation d'un personnage. Dans les méthodes classiques, un poids associé à chaque articulation doit être attaché à chaque sommet du maillage du personnage, ce qui est souvent très coûteux en temps lorsqu'une animation précise est exigée. Nous proposons une nouvelle modélisation du processus de skinning, s'inspirant de la notion d'atlas de cartes. A partir du squelette d'animation du personnage, nous décomposons d'abord automatiquement le maillage en régions anatomiquement significatives et qui se chevauchent. Ces régions sont ensuite fusionnées dans leurs zones de chevauchement grˆace à l'utilisation de fonctions de transition continues. Ceci conduit à un processus de skinning simple mais néanmoins efficace, pour lequel les poids sont automatiquement définis et ne dépendent pas de la distance euclidienne entre sommets, mais de la distance sur la surface

A spectral clustering approach of vegetation components for describing plant topology and geometry from terrestrial waveform LiDAR data

PosterInternational audienceComputer models that treat plant architectures as a collection of interconnected elementary units (internode, petiole, leaf lamina), which are spatially distributed within the above- and/or the below-ground space, have become increasingly popular in the FSPM scientific community (DeJong et al. 2011). The core of such 3-D plant architecture models deal with contrasting reconstruction methods generally based on stochastic, fractal or L-system approaches, or by describing accurately the geometry of each plant component in situ using 3-D digitizing technology. These methods can approximate the geometry of many species for understanding and integrating plant development and ecophysiology, but have generally been applied at a small scale. High-resolution terrestrial Light Detection And Ranging (tLiDAR), a 3-D remote sensing technique, has recently been applied for measuring the 3-D characteristics of vegetation from grass to forest plant species (Dassot et al. 2011). The resulting data are known as a point cloud which shows the 3-D position of all the hits by the laser beam giving a raw sketch of the spatial distribution of plant elements in 3-D, but without explicit information on their geometry and connectivity. In this study we propose a new approach based on a delineation algorithm that clusters a point cloud into elementary plant units. The algorithm creates a graph (points + edges) to recover plausible neighbouring relationships between the points and embed this graph in a spectral space in order to segment the point-cloud into meaningful elementary plant units. Our approach is robust to inherent geometric outliers and/or noisy points and only considers the x, y, z coordinate tLiDAR data as an input

Temporally coherent mesh sequence segmentations

In this report, we consider the problem of fully automatic segmentation of mesh sequences, with or without temporal coherence. More precisely, our goal is to identify model parts that consistently move rigidly over time. We propose a novel framework that incrementally adapts segments along a sequence based on the coherence of motion information within each segment. In contrast to existing approaches, this framework handles meshes independently reconstructed at each time instant, provided that motion cues are available. It allows therefore for meshes with varying connectivity as well as varying topology. Experiments on various data sets in addition to a quantitative evaluation demonstrate the effectiveness and robustness of the approach.Nous considérons dans ce rapport le problème de la segmentation entièrement automatique de séquences de maillages, avec ou sans cohérence temporelle. Plus précisément, notre but est d'identifier les parties d'un modèle qui se déplacent rigidement de manière cohérente au cours du temps. Nous proposons un canevas nouveau pour adapter ces régions de manière incrémentale le long de la séquence, en se basant sur la cohérence de l'information de mouvement dans chaque région. Contrairement aux approches existantes, ce canevas permet de traiter les séquences de maillages reconstruits indépendamment à chaque pas de temps, pourvu que des indicateurs de mouvement soient disponibles. Il permet donc de segmenter des maillages avec changement de connectivité et/ou changement de topologie. Des expériences sur plusieurs jeux de données ainsi qu'une évaluation quantitative démontrent l'efficacité ainsi que la robustesse de cette approche

Mesh Repair with Topology Control

In this research report, we propose a new method to convert a triangular mesh with geometrical and topological defects into a 2-manifold, whose topology (genus and number of connected components) is controlled by the user. We start by converting the input mesh into a thin layer of face-connected voxels; then the topology of this voxel set can be modified by the user thanks to morphological operators of different orders; at last the fixed voxel set is converted back into a triangular mesh, which both is a 2-manifold and have the desired topology

Estimation of Human Body Shape in Motion with Wide Clothing

International audienceEstimating 3D human body shape in motion from a sequence of unstructured oriented 3D point clouds is important for many applications. We propose the first automatic method to solve this problem that works in the presence of loose clothing. The problem is formulated as an optimization problem that solves for identity and posture parameters in a shape space capturing likely body shape variations. The automation is achieved by leveraging a recent robust pose detection method Stitched Puppet. To account for clothing, we take advantage of motion cues by encouraging the estimated body shape to be inside the observations. The method is evaluated on a new benchmark containing different subjects, motions, and clothing styles that allows to quantitatively measure the accuracy of body shape estimates. Furthermore, we compare our results to existing methods that require manual input and demonstrate that results of similar visual quality can be obtained