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

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

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

A 3D+t Laplace operator for temporal mesh sequences

International audienceThe Laplace operator plays a fundamental role in geometry processing. Several discrete versions have been proposed for 3D meshes and point clouds, among others. We define here a discrete Laplace operator for temporally coherent mesh sequences, which allows to process mesh animations in a simple yet efficient way. This operator is a discretization of the Laplace-Beltrami operator using Discrete Exterior Calculus on CW complexes embedded in a four-dimensional space. A parameter is introduced to tune the influence of the motion with respect to the geometry. This enables straightforward generalization of existing Laplacian static mesh processing works to mesh sequences. An application to spacetime editing is provided as example

Méthodes de segmentation et par squelette pour la compréhension numérique de formes géométriques

Digitised geometric shape models are essentially represented as a collection of primitives without a general coherence. Shape understanding aims at retrieving global information about the shape geometry, topology or functionality, for subsequent uses such as measurement, simulation or modification. In this context, this manuscript presents my main contributions to digital shape understanding, which are mostly based on shapedecomposition and skeleton computation. The first part explores the faithfulness of the 3D mesh representation to the real world object, through topological and perceptual analyses, and suggests a conversion to a regular volumetric model. The second part focuses on shapes in motion and details tools to create, modify and analyse temporal mesh sequences. The third part explains through two concrete examples how digitalshape understanding can help experts in medicine and forestry. Finally, three open questions for shape understanding of shapes in motion and scanned trees are discussed by way of perspectives.Les modèles géométriques de formes numérisées sont pour l’essentiel représentés comme une collection de primitives sans cohérence générale. La compréhension de formes a pour but de retrouver une information globale concernant la géométrie, topologie ou fonction d’une forme, afin de pouvoir la mesurer, la modifier ou l’utiliser en simulation. Dans ce contexte, ce manuscript présente mes principales contributions à la compréhension numérique de formes géométriques, qui sont principalement fondées sur la décomposition de formes et le calcul de squelette. La première partie explore la fidélité de la représentation par maillage 3D à l’objet du monde réel, à travers des analyses topologiques et perceptuelles, et propose une conversion vers un modèle volumique régulier. La deuxième partie se concentre sur les formes en mouvement et détaille des outils permettant de créer, modifier et analyser des séquences temporelles de maillages. La troisième partie explique à travers deux exemples concrets comment la compréhension numérique de formes peut aider les experts dans des domaines comme la médecine et la sylviculture. Enfin, trois questions ouvertes sur la compréhension de formes pour les formes en mouvement et les arbres scannés sont discutées en guise de perspectives

Méthodes de segmentation et par squelette pour la compréhension numérique de formes géométriques

Digitised geometric shape models are essentially represented as a collection of primitives without a general coherence. Shape understanding aims at retrieving global information about the shape geometry, topology or functionality, for subsequent uses such as measurement, simulation or modification. In this context, this manuscript presents my main contributions to digital shape understanding, which are mostly based on shapedecomposition and skeleton computation. The first part explores the faithfulness of the 3D mesh representation to the real world object, through topological and perceptual analyses, and suggests a conversion to a regular volumetric model. The second part focuses on shapes in motion and details tools to create, modify and analyse temporal mesh sequences. The third part explains through two concrete examples how digitalshape understanding can help experts in medicine and forestry. Finally, three open questions for shape understanding of shapes in motion and scanned trees are discussed by way of perspectives.Les modèles géométriques de formes numérisées sont pour l’essentiel représentés comme une collection de primitives sans cohérence générale. La compréhension de formes a pour but de retrouver une information globale concernant la géométrie, topologie ou fonction d’une forme, afin de pouvoir la mesurer, la modifier ou l’utiliser en simulation. Dans ce contexte, ce manuscript présente mes principales contributions à la compréhension numérique de formes géométriques, qui sont principalement fondées sur la décomposition de formes et le calcul de squelette. La première partie explore la fidélité de la représentation par maillage 3D à l’objet du monde réel, à travers des analyses topologiques et perceptuelles, et propose une conversion vers un modèle volumique régulier. La deuxième partie se concentre sur les formes en mouvement et détaille des outils permettant de créer, modifier et analyser des séquences temporelles de maillages. La troisième partie explique à travers deux exemples concrets comment la compréhension numérique de formes peut aider les experts dans des domaines comme la médecine et la sylviculture. Enfin, trois questions ouvertes sur la compréhension de formes pour les formes en mouvement et les arbres scannés sont discutées en guise de perspectives

A point-cloud classification method to assess biases in tLiDAR-based forest canopy gap fraction estimates

International audienc

Just Noticeable Distortion Profile for Flat-Shaded 3D Mesh Surfaces

International audienceIt is common that a 3D mesh undergoes some lossy operations (e.g., compression, watermarking and transmission through noisy channels), which can introduce geometric distortions as a change in vertex position. In most cases the end users of 3D meshes are human beings; therefore, it is important to evaluate the visibility of introduced vertex displacement. In this paper we present a model for computing a Just Noticeable Distortion (JND) profile for flat-shaded 3D meshes. The proposed model is based on an experimental study of the properties of the human visual system while observing a flat-shaded 3D mesh surface, in particular the contrast sensitivity function and contrast masking. We first define appropriate local perceptual properties on 3D meshes. We then detail the results of a series of psychophysical experiments where we have measured the threshold needed for a human observer to detect the change in vertex position. These results allow us to compute the JND profile for flat-shaded 3D meshes. The proposed JND model has been evaluated via a subjective experiment, and applied to guide 3D mesh simplification as well as to determine the optimal vertex coordinates quantization level for a 3D model