Abstract: Merging meshes is a recurrent need in geometry modeling and it is a critical step in the 3D acquisition pipeline, where it is used for building a single mesh from several range scans. A pioneering simple and effective solution to merging is represented by the Zippering algorithm (Turk and Levoy, 1994), which consists of simply stitching the meshes together along their borders. In this paper we propose a new extended version of the zippering algorithm that enables the user to control the resulting mesh by introducing quality criteria in the selection of redundant data, and allows to zip together meshes with different granularity by an ad hoc refinement algorithm.
Authors: S. Marras, F. Ganovelli, P. Cignoni, R. Scateni, R. Scopigno.
Controlled and Adaptive Mesh Zippering.
VisiGRAPP 2010, 104-109.
Angers, Francia, Maggio 2010.
Abstract: The Education Programme at Eurographics 2009 took place in Munich, Germany, over the course of 2 days: March 31st and April 1st 2009. Educators were invited to present their experience in teaching computer graphics over a wide range of topics: from teaching mathematical foundations of computer graphics to using visual tools; from teaching in a strict computer science curriculum to teaching students of mixed disciplines and on to teaching in other curricula. As a result, we had 12 presentations in four sessions, ranging from a new method of teaching quaternions to teaching computer graphics in the context of theatre. The presence of 20–40 attendees throughout these 2 days made clear that the Education Programme at Eurographics has established itself over the last years.
Abstract: This paper describes the work that has been done during the first year of the 3-SHIRT project, which aims at developing innovative solutions in all the phases of content-based 3D shape retrieval, namely: shape analysis and segmentation, design of shape descriptors, shape indexing and matching, and evaluation.
Abstract: Recent advances in acquisition and modelling techniques led to generating an exponentially increasing amount of 3D shapes available both over the Internet or in specific databases. While the number grows it becomes more and more difficult to keep an organized knowledge over the content of this repositories. It is commonly intended that in the near future 3D shapes and models will be indexed and searched using procedure and instruments mimicking the same operations performed on images while using algorithms, data structures and instruments peculiar to the domain. In this context it is thus important to have tools for automatic characterization of 3D shapes, and skeletons and partitions are the two most prominent ones among them. In this paper we will describe an experience of building some of this tools on the top of a popular and robust library for manipulating meshes (OpenMesh). The preliminary results we present are promising enough to let us expect that the sum of the tools will be a useful aid to improving indexing and retrieval of digital 3D objects. The work presented here is part of a larger project: Three-Dimensional Shape Indexing and Retrieval Techniques (3-SHIRT), in collaboration with the Universities of Genoa, Padua, Udine, and Verona.
Abstract: Understanding when a cloud of points in three-dimensional space can be, semantically, interpreted as a surface, and then being able to describe the surface, is an interesting problem in itself and an important task to tackle in several application fields. Finding a possible solution to the problem implies to answer to many typical questions about surface acquisition and mesh reconstruction: how one can build a metric telling whether a point in space belongs to the surface? Given data from 3D scanning devices, how can we tell apart (and eventually discard) points representing noise from signal? Can the reached insight be used to align point clouds coming from different acquisitions? Inside this framework, the present paper investigates the features of a new dimensional clustering algorithm. Unless standard clustering methods, the peculiarity of this algorithm is, using the local fractal dimension, to select subsets of lower dimensionality inside the global of dimension N. When applied to the study of discrete surfaces embedded in three dimensional space, the algorithm results to be robust and able to discriminate the surface as a subset of fractal dimension two, differentiating it from the background, even in the presence of an intense noise. The preliminary tests we performed, on points clouds generated from known surfaces, show that the recognition error is lower than 3 percent and does not affect the visual quality of the final result.