Etat de l'art de la segmentation de maillage 3D par patchs surfaciques

National audienceLa segmentation de maillage 3D est une composante essentielle de nombreuses applications. Elle se décline en deux familles : la segmentation en patchs surfaciques et la segmentation en parties significatives. Dans cet article, nous traitons principalement de la segmentation de maillages 3D en patchs surfaciques et en proposons un état de l'art. Nous positionnons le contexte de ce type de segmentation et discutons des contributions les plus pertinentes

Deformable meshes for shape recovery: models and applications

With the advance of scanning and imaging technology, more and more 3D objects become available. Among them, deformable objects have gained increasing interests. They include medical instances such as organs, a sequence of objects in motion, and objects of similar shapes where a meaningful correspondence can be established between each other. Thus, it requires tools to store, compare, and retrieve them. Many of these operations depend on successful shape recovery. Shape recovery is the task to retrieve an object from the environment where its geometry is hidden or implicitly known. As a simple and versatile tool, mesh is widely used in computer graphics for modelling and visualization. In particular, deformable meshes are meshes which can take the deformation of deformable objects. They extend the modelling ability of meshes. This dissertation focuses on using deformable meshes to approach the 3D shape recovery problem. Several models are presented to solve the challenges for shape recovery under different circumstances. When the object is hidden in an image, a PDE deformable model is designed to extract its surface shape. The algorithm uses a mesh representation so that it can model any non-smooth surface with an arbitrary precision compared to a parametric model. It is more computational efficient than a level-set approach. When the explicit geometry of the object is known but is hidden in a bank of shapes, we simplify the deformation of the model to a graph matching procedure through a hierarchical surface abstraction approach. The framework is used for shape matching and retrieval. This idea is further extended to retain the explicit geometry during the abstraction. A novel motion abstraction framework for deformable meshes is devised based on clustering of local transformations and is successfully applied to 3D motion compression

Detail Enhancing Denoising of Digitized 3D Models from a Mobile Scanning System

The acquisition process of digitizing a large-scale environment produces an enormous amount of raw geometry data. This data is corrupted by system noise, which leads to 3D surfaces that are not smooth and details that are distorted. Any scanning system has noise associate with the scanning hardware, both digital quantization errors and measurement inaccuracies, but a mobile scanning system has additional system noise introduced by the pose estimation of the hardware during data acquisition. The combined system noise generates data that is not handled well by existing noise reduction and smoothing techniques. This research is focused on enhancing the 3D models acquired by mobile scanning systems used to digitize large-scale environments. These digitization systems combine a variety of sensors – including laser range scanners, video cameras, and pose estimation hardware – on a mobile platform for the quick acquisition of 3D models of real world environments. The data acquired by such systems are extremely noisy, often with significant details being on the same order of magnitude as the system noise. By utilizing a unique 3D signal analysis tool, a denoising algorithm was developed that identifies regions of detail and enhances their geometry, while removing the effects of noise on the overall model. The developed algorithm can be useful for a variety of digitized 3D models, not just those involving mobile scanning systems. The challenges faced in this study were the automatic processing needs of the enhancement algorithm, and the need to fill a hole in the area of 3D model analysis in order to reduce the effect of system noise on the 3D models. In this context, our main contributions are the automation and integration of a data enhancement method not well known to the computer vision community, and the development of a novel 3D signal decomposition and analysis tool. The new technologies featured in this document are intuitive extensions of existing methods to new dimensionality and applications. The totality of the research has been applied towards detail enhancing denoising of scanned data from a mobile range scanning system, and results from both synthetic and real models are presented

Erzeugung von 3D-Netzmodellen in der Produktentwicklung durch Deformation initialer 3D-Netzmodelle

Mit 3D-Netzmodellen werden Objekte der materiellen Welt oder unserer Vorstellung computergestützt abgebildet. In digitalen Produktentwicklungsprozessen werden mit ihnen sowohl die Objektgestalt als auch anwendungsspezifische Informationen von Objekten und von Prozessen definiert. Mit flächenhaften Netzen (z. B. Dreiecksnetze) wird die Oberfläche von Objekten in diskreter Form repräsentiert, mit volumenhaften Netzen (z. B. Tetraedernetze) zusätzlich das Objektinnere. 3D-Netzmodelle werden bei der Erzeugung und der Manipulation, der Analyse und der Validierung, in fertigungsvorbereitenden Prozessen sowie zur Präsentation digitaler 3D-Objekte angewandt