GPU-based Streaming for Parallel Level of Detail on Massive Model Rendering

Rendering massive 3D models in real-time has long been recognized as a very challenging problem because of the limited computational power and memory space available in a workstation. Most existing rendering techniques, especially level of detail (LOD) processing, have suffered from their sequential execution natures, and does not scale well with the size of the models. We present a GPU-based progressive mesh simplification approach which enables the interactive rendering of large 3D models with hundreds of millions of triangles. Our work contributes to the massive rendering research in two ways. First, we develop a novel data structure to represent the progressive LOD mesh, and design a parallel mesh simplification algorithm towards GPU architecture. Second, we propose a GPU-based streaming approach which adopt a frame-to-frame coherence scheme in order to minimize the high communication cost between CPU and GPU. Our results show that the parallel mesh simplification algorithm and GPU-based streaming approach significantly improve the overall rendering performance

3D Mesh Simplification. A survey of algorithms and CAD model simplification tests

Simplification of highly detailed CAD models is an important step when CAD models are visualized or by other means utilized in augmented reality applications. Without simplification, CAD models may cause severe processing and storage is- sues especially in mobile devices. In addition, simplified models may have other advantages like better visual clarity or improved reliability when used for visual pose tracking. The geometry of CAD models is invariably presented in form of a 3D mesh. In this paper, we survey mesh simplification algorithms in general and focus especially to algorithms that can be used to simplify CAD models. We test some commonly known algorithms with real world CAD data and characterize some new CAD related simplification algorithms that have not been surveyed in previous mesh simplification reviews.Siirretty Doriast

Mesh simplification with hierarchical shape analysis and iterative edge contraction

2003-2004 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Signal-Specialized Parameterization

To reduce memory requirements for texture mapping a model, we build a surface parametrization specialized to its signal (such as color or normal). Intuitively, we want to allocate more texture samples in regions with greater signal detail. Our approach is to minimize signal approximation error --- the difference between the original surface signal and its reconstruction from the sampled texture. Specifically, our signal-stretch parametrization metric is derived from a Taylor expansion of signal error. For fast evaluation, this metric is pre-integrated over the surface as a metric tensor. We minimize this nonlinear metric using a novel coarse-to-fine hierarchical solver, further accelerated with a fine-to-coarse propagation of the integrated metric tensor. Use of metric tensors permits anisotropic squashing of the parametrization along directions of low signal gradient. Texture area can often be reduced by a factor of 4 for a desired signal accuracy compared to non-specialized parametrizations.Engineering and Applied Science

Topology Preserving Simplification of 2D Non-Manifold Meshes with Embedded Structures

International audienceMesh simplification has received tremendous attention over the past years. Most of the previous works deal with a proper choice of error measures to guide the simplification. Preserving the topological characteristics of the mesh and possibly of data attached to the mesh is a more recent topic, the present paper is about.We introduce a new topology preserving simplification algorithm for triangular meshes, possibly non-manifold, with embedded polylines. In this context embedded means that the edges of the polylines are also edges of the mesh. The paper introduces a robust test to detect if the collapse of an edge in the mesh modifies either the topology of the mesh or the topology of the embedded polylines. This validity test is derived using combinatorial topology results. More precisely we define a so-called extended complex from the input mesh and the embedded polylines. We show that if an edge collapse of the mesh preserves the topology of this extended complex, then it also preserves both the topology of the mesh and the embedded polylines. Our validity test can be used for any 2-complex mesh, including non-manifold triangular meshes. It can be combined with any previously introduced error measure. Implementation of this validity test is described. We demonstrate the power and versatility of our method with scientific data sets from neuroscience, geology and CAD/CAM models from mechanical engineering

A Comparative Study on Polygonal Mesh Simplification Algorithms

Polygonal meshes are a common way of representing three dimensional surface models in many different areas of computer graphics and geometry processing. However, with the evolution of the technology, polygonal models are becoming more and more complex. As the complexity of the models increase, the visual approximation to the real world objects get better but there is a trade-off between the cost of processing these models and better visual approximation. In order to reduce this cost, the number of polygons in a model can be reduced by mesh simplification algorithms. These algorithms are widely used such that nearly all of the popular mesh editing libraries include at least one of them. In this work, polygonal simplification algorithms that are embedded in open source libraries: CGAL, VTK and OpenMesh are compared with the Metro geometric error measuring tool. By this way we try to supply a guidance for developers for publicly available mesh libraries in order to implement polygonal mesh simplification

Preserving attribute values on simplified meshes by re-sampling detail textures

Many sophisticated solutions have been proposed to reduce the geometric complexity of 3D meshes. A slightly less studied problem is how to preserve attribute detail on simplified meshes (e.g., color, high-frequency shape details, scalar fields, etc.).We present a general approach that is completely independent of the simplification technique adopted to reduce the mesh size. We use resampled textures (rgb, bump, displacement or shade maps) to decouple attribute detail representation from geometry simplification. The original contribution is that preservation is performed after simplification by building a set of triangular texture patches that are then packed into a single texture map. This general solution can be applied to the output of any topology-preserving simplification code and it allows any attribute value defined on the high-resolution mesh to be recovered. Moreover, decoupling shape simplification from detail preservation (and encoding the latter with texture maps) leads to high simplification rates and highly efficient rendering. We also describe an alternative application: the conversion of 3D models with 3D procedural textures (which generally force the use of software renderers) into standard 3D models with 2D bitmap textures

Kolmiulotteisten tietokoneavusteisten mallien yksinkertaistaminen renderoinnin nopeuttamiseksi

Visualization of three-dimensional (3D) computer-aided design model is an integral part of the design process. Large assemblies such as plant or building designs contain a substantial amount of geometric data. New constraints for visualization performance and the amount of geometric data are set by the advent of mobile devices and virtual reality headsets. Our goal is to improve visualization performance and reduce memory consumption by simplifying 3D models while retaining the output simplification quality stable regardless of the geometric complexity of the input mesh. We research the current state of 3D mesh simplification methods that use geometry decimation. We design and implement our own data structure for geometry decimation. Based on the existing research, we select and use an edge decimation method for model simplification. In order to free the user from configuring edge decimation level per model by hand, and to retain a stable quality of the simplification output, we propose a threshold parameter, \textit{edge decimation cost threshold}. The threshold is calculated by multiplying the length of the model’s bounding box diagonal with a user-defined scale parameter. Our results show that the edge decimation cost threshold works as expected. The geometry decimation algorithm manages to simplify models with round surfaces with an excellent simplification rate. Based on the edge decimation cost threshold, the algorithm terminates the geometry decimation for models that have a large number of planar surfaces. Without the threshold, the simplification leads to large geometric errors quickly. The visualization performance improvement from the simplification scales almost at the same rate as the simplification rate