Abstract: Recently, the number of applications developed for smartphones has dramatically increased; however, at the moment, applications having the purpose of creating and displaying 3D models are quite rare. The goal of this work is to build an application that allows the user to see the virtual three-dimensional representations of their friends and interact with them. The main challenge is to achieve results similar to those that a computer would produce, optimizing the process to deal with the constraints due to the technology used. Since there are no similar mobile applications, this work will make possible to create a base onto which will be possible to realize applications that have customized 3D models as a common feature.
Authors: S. Boi, F. Sorrentino, S. Marras, R. Scateni.
3D-ize U! A Real-time 3D Head-model Texture Generator for Android.
EuroGraphics Italian Chapter 2011, 41-46.
Salerno, Italia, Novembre 2011.
Abstract: The advent of GPGPU technologies has allowed for sensible speed-ups in many high-dimension, memory-intensive computational problems. In this paper we demonstrate the effectiveness of such techniques by describing two applications of GPGPU computing to two different subfields of computer graphics, namely computer vision and mesh processing. In the first case, CUDA technology is employed to accelerate the computation of approximation of motion between two images, known also as optical flow. As for mesh processing, we exploit the massively parallel architecture of CUDA devices to accelerate the face clustering procedure that is employed in many recent mesh segmentation algorithms. In both cases, the results obtained so far are presented and thoroughly discussed, along with the expected future development of the work.
Abstract: In this paper we propose an optimization of the Shape Diameter Function (SDF) that we call Accelerated SDF (ASDF). We discuss in detail the advantages and disadvantages of the original SDF definition, proposing theoretical and practical approaches for speedup and approximation. Using Poisson-based interpolation we compute the SDF value for a small subset of randomly distributed faces and propagate the values over the mesh. We show the results obtained with ASDF versus SDF in terms of timings and error.
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.
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.