Shape Animation with Combined Captured and Simulated Dynamics

We present a novel volumetric animation generation framework to create new types of animations from raw 3D surface or point cloud sequence of captured real performances. The framework considers as input time incoherent 3D observations of a moving shape, and is thus particularly suitable for the output of performance capture platforms. In our system, a suitable virtual representation of the actor is built from real captures that allows seamless combination and simulation with virtual external forces and objects, in which the original captured actor can be reshaped, disassembled or reassembled from user-specified virtual physics. Instead of using the dominant surface-based geometric representation of the capture, which is less suitable for volumetric effects, our pipeline exploits Centroidal Voronoi tessellation decompositions as unified volumetric representation of the real captured actor, which we show can be used seamlessly as a building block for all processing stages, from capture and tracking to virtual physic simulation. The representation makes no human specific assumption and can be used to capture and re-simulate the actor with props or other moving scenery elements. We demonstrate the potential of this pipeline for virtual reanimation of a real captured event with various unprecedented volumetric visual effects, such as volumetric distortion, erosion, morphing, gravity pull, or collisions

Shape Animation with Combined Captured and Simulated Dynamics

We present a novel volumetric animation generation framework to create new types of animations from raw 3D surface or point cloud sequence of captured real performances. The framework considers as input time incoherent 3D observations of a moving shape, and is thus particularly suitable for the output of performance capture platforms. In our system, a suitable virtual representation of the actor is built from real captures that allows seamless combination and simulation with virtual external forces and objects, in which the original captured actor can be reshaped, disassembled or reassembled from user-specified virtual physics. Instead of using the dominant surface-based geometric representation of the capture, which is less suitable for volumetric effects, our pipeline exploits Centroidal Voronoi tessellation decompositions as unified volumetric representation of the real captured actor, which we show can be used seamlessly as a building block for all processing stages, from capture and tracking to virtual physic simulation. The representation makes no human specific assumption and can be used to capture and re-simulate the actor with props or other moving scenery elements. We demonstrate the potential of this pipeline for virtual reanimation of a real captured event with various unprecedented volumetric visual effects, such as volumetric distortion, erosion, morphing, gravity pull, or collisions

Shape Animation with Combined Captured and Simulated Dynamics

We present a novel volumetric animation generation framework to create new types of animations from raw 3D surface or point cloud sequence of captured real performances. The framework considers as input time incoherent 3D observations of a moving shape, and is thus particularly suitable for the output of performance capture platforms. In our system, a suitable virtual representation of the actor is built from real captures that allows seamless combination and simulation with virtual external forces and objects, in which the original captured actor can be reshaped, disassembled or reassembled from user-specified virtual physics. Instead of using the dominant surface-based geometric representation of the capture, which is less suitable for volumetric effects, our pipeline exploits Centroidal Voronoi tessellation decompositions as unified volumetric representation of the real captured actor, which we show can be used seamlessly as a building block for all processing stages, from capture and tracking to virtual physic simulation. The representation makes no human specific assumption and can be used to capture and re-simulate the actor with props or other moving scenery elements. We demonstrate the potential of this pipeline for virtual reanimation of a real captured event with various unprecedented volumetric visual effects, such as volumetric distortion, erosion, morphing, gravity pull, or collisions

Méthodes de partitionnement de surfaces

L'OBJET DE NOTRE TRAVAIL EST DE FOURNIR DE NOUVELLES TECHNIQUES DE DECOMPOSITION DE SURFACES POLYEDRIQUES EN SOUS-ENTITES POUVANT ETRETRAITEES SEPAREMMENT DE MANIERE PLUS EFFICACE. NOUS PROPOSONS DEUX TYPES DE PARTITIONS DE SURFACES TRIANGULEES FERMEES. LE PREMIER EST UNE DECOMPOSITION EN QUADRANGLES DE LA SURFACE, BASEE SUR UNE REPRESENTATION DE SON INFORMATION TOPOLOGIQUE : LES ARETES DES QUADRANGLES SONT CONSTRUITES A PARTIR DU PLONGEMENT SUR LA SURFACE D'UN ENSEMBLE CANONIQUE DE SES LACETS GENERATEURS. LE SECOND EST BASE SUR LA DETECTION DE GEODESIQUES PERIODIQUES DE LONGUEUR LOCALEMENT MINIMALE ("ETRANGLEMENTS") SUR LA SURFACE, PAR SIMPLIFICATION PROGRESSIVE DE LA TRIANGULATION ET CONSTRUCTION DE GEODESIQUES PAR MORCEAUX FERMEES.GRENOBLE1-BU Sciences (384212103) / SudocSudocFranceF

Geometric and Topological Mesh Feature Extraction for 3D Shape Analysis

International audienceThree-dimensional surface meshes are the most common discrete representation of the exterior of a virtual shape. Extracting relevant geometric or topological features from them can simplify the way objects are looked at, help with their recognition, and facilitate description and categorization according to specific criteria. This book adopts the point of view of discrete mathematics, the aim of which is to propose discrete counterparts to concepts mathematically defined in continuous terms. It explains how standard geometric and topological notions of surfaces can be calculated and computed on a 3D surface mesh, as well as their use for shape analysis. Several applications are also detailed, demonstrating that each of them requires specific adjustments to fit with generic approaches. The book is intended not only for students, researchers and engineers in computer science and shape analysis, but also numerical geologists, anthropologists, biologists and other scientists looking for practical solutions to their shape analysis, understanding or recognition problems

Géométrie et topologie pour les maillages 3D

National audienceLes formes géométriques, qu’elles proviennent du monde naturel ou du monde manufacturé par l’Homme, ont tendance à être de plus en plus numérisées, cela, entre autres, à des fins de visualisation ou de mesure. Ce processus produit en général des maillages 3D, composés d’une multitude de polygones plans. Ces maillages sont la représentation discrète la plus commune pour caractériser la surface d’une forme virtuelle. Ces représentations surfaciques 3D sont traitées le plus souvent de manière automatique, parfois interactive, afin que leur structure globale ou certains détails soient analysés ou calculés. Cela peut être fait en extrayant des caractéristiques géométriques ou topologiques pertinentes. De telles caractéristiques de forme peuvent simplifier la façon dont l’objet est considéré, elles peuvent aider à la reconnaissance, et elles peuvent le décrire et le classifier selon des critères spécifiques. Ce cours traitera de la définition et du calcul de caractéristiques sur un maillage surfacique 3D et de leur utilisation pour l’analyse de forme. Des méthodes récentes seront décrites pour extraire des caractéristiques ayant une signification liée non seulement à la géométrie mais aussi à la topologie. Plusieurs applications seront développées au cours des travaux pratiques

Sensitivity analysis of an automated processing chain and uncertainty in the prediction of tree above ground biomass from TLS data

International audienceThe above ground volume (AGV) measurement of a sampled tree is a fundamental input to provide predictions of forest, woodland and urban resources, but it is generally biased by country-specific merchantable thresholds. Terrestrial laser scanners (TLS) have been demonstrated to be promising for non-destructive and accurate measurements. Actually, there have been recent procedural approaches to develop automated processing chains for extracting tree metrics from TLS data. A sensitivity analysis of an automated chain on 12 parameters is presented here to report effects of TLS and scan acquisition characteristics and routines used for data filtering and volume estimates on AGBiomass predictions. This analysis was based on data recorded by a Leica HDS-6100 on Oak, Hornbeam, Birch and Larch during winters 2014-16. Three trees were recorded per spp. from six scan positions around each tree and with three TLS sampling resolutions (0.072-0.018°) per position. Scanned trees were felled, then measured in detail and stratified into lower stem (Ls), coarse (Cb, diameter ge 7 cm) and small (Sb, lt 7 cm) branch sections. When compared against ground data, this analysis indicated a consistent pattern across all trees for DBH (r²=0.98, bias<0.001 m), tree height (r²=0.89, bias>-0.63 m) or AGBs (r²=0.98, bias<4; r²=0.99, bias>-34; r²=0.96, bias<8 kg for Ls, Cb and Sb, respectively) with a TLS resolution of 0.018° driving improved fits for h (+5%), AGBCb (+13%) and AGBSb (+27%) and 6 scan positions driving improved fits for AGBCb (+56%) and AGBSb (+36%). The quality of the filter routine was found to be the most critical parameter (up-to ±65% for Sb). All other parameters had a relatively little effect

Sensitivity analysis of an automated processing chain and uncertainty in the prediction of tree above ground biomass from TLS data

International audienceThe above ground volume (AGV) measurement of a sampled tree is a fundamental input to provide predictions of forest, woodland and urban resources, but it is generally biased by country-specific merchantable thresholds. Terrestrial laser scanners (TLS) have been demonstrated to be promising for non-destructive and accurate measurements. Actually, there have been recent procedural approaches to develop automated processing chains for extracting tree metrics from TLS data. A sensitivity analysis of an automated chain on 12 parameters is presented here to report effects of TLS and scan acquisition characteristics and routines used for data filtering and volume estimates on AGBiomass predictions. This analysis was based on data recorded by a Leica HDS-6100 on Oak, Hornbeam, Birch and Larch during winters 2014-16. Three trees were recorded per spp. from six scan positions around each tree and with three TLS sampling resolutions (0.072-0.018°) per position. Scanned trees were felled, then measured in detail and stratified into lower stem (Ls), coarse (Cb, diameter ge 7 cm) and small (Sb, lt 7 cm) branch sections. When compared against ground data, this analysis indicated a consistent pattern across all trees for DBH (r²=0.98, bias-0.63 m) or AGBs (r²=0.98, bias-34; r²=0.96, bias<8 kg for Ls, Cb and Sb, respectively) with a TLS resolution of 0.018° driving improved fits for h (+5%), AGBCb (+13%) and AGBSb (+27%) and 6 scan positions driving improved fits for AGBCb (+56%) and AGBSb (+36%). The quality of the filter routine was found to be the most critical parameter (up-to ±65% for Sb). All other parameters had a relatively little effect

Sensitivity analysis of an automated processing chain and uncertainty in the prediction of tree above ground biomass from TLS data

International audienceThe above ground volume (AGV) measurement of a sampled tree is a fundamental input to provide predictions of forest, woodland and urban resources, but it is generally biased by country-specific merchantable thresholds. Terrestrial laser scanners (TLS) have been demonstrated to be promising for non-destructive and accurate measurements. Actually, there have been recent procedural approaches to develop automated processing chains for extracting tree metrics from TLS data. A sensitivity analysis of an automated chain on 12 parameters is presented here to report effects of TLS and scan acquisition characteristics and routines used for data filtering and volume estimates on AGBiomass predictions. This analysis was based on data recorded by a Leica HDS-6100 on Oak, Hornbeam, Birch and Larch during winters 2014-16. Three trees were recorded per spp. from six scan positions around each tree and with three TLS sampling resolutions (0.072-0.018°) per position. Scanned trees were felled, then measured in detail and stratified into lower stem (Ls), coarse (Cb, diameter ge 7 cm) and small (Sb, lt 7 cm) branch sections. When compared against ground data, this analysis indicated a consistent pattern across all trees for DBH (r²=0.98, bias-0.63 m) or AGBs (r²=0.98, bias-34; r²=0.96, bias<8 kg for Ls, Cb and Sb, respectively) with a TLS resolution of 0.018° driving improved fits for h (+5%), AGBCb (+13%) and AGBSb (+27%) and 6 scan positions driving improved fits for AGBCb (+56%) and AGBSb (+36%). The quality of the filter routine was found to be the most critical parameter (up-to ±65% for Sb). All other parameters had a relatively little effect

A Graph-Based Approach for Simultaneous Semantic and Instance Segmentation of Plant 3D Point Clouds

International audienceAccurate simultaneous semantic and instance segmentation of a plant 3D point cloud is critical for automatic plant phenotyping. Classically, each organ of the plant is detected based on the local geometry of the point cloud, but the consistency of the global structure of the plant is rarely assessed. We propose a two-level, graph-based approach for the automatic, fast and accurate segmentation of a plant into each of its organs with structural guarantees. We compute local geometric and spectral features on a neighbourhood graph of the points to distinguish between linear organs (main stem, branches, petioles) and two-dimensional ones (leaf blades) and even 3-dimensional ones (apices). Then a quotient graph connecting each detected macroscopic organ to its neighbors is used both to refine the labelling of the organs and to check the overall consistency of the segmentation. A refinement loop allows to correct segmentation defects. The method is assessed on both synthetic and real 3D point-cloud data sets of Chenopodium album (wild spinach) and Solanum lycopersicum (tomato plant)