External Memory View-Dependent Simplification

2002

In this paper we are presenting a novel approach for rendering large datasets in a view-dependent manner. In a typical view-dependent rendering framework, an appropriate level of detail is selected and sent to the graphics hardware for rendering at each frame. In our approach, we have successfully managed to speed up the selection of the level of detail as well as the rendering of the selected levels. We have accelerated the selection of the appropriate level of detail by not scanning active nodes that do not contribute to the incremental update of the selected level of detail. Our idea is based on imposing a spatial subdivision over the view-dependence trees data-structure, which allows spatial tree cells to refine and merge in real-time rendering to comply with the changes in the active nodes list. The rendering of the selected level of detail is accelerated by using vertex arrays. To overcome the dynamic changes in the selected levels of detail we use multiple small vertex arrays whose sizes depend on the memory on the graphics hardware. These multiple vertex arrays are attached to the active cells of the spatial tree and represent the active nodes of these cells. These vertex arrays, which are sent to the graphics hardware at each frame, merge and split with respect to the changes in the cells of the spatial tree.

IEEE Transactions on Visualization and Computer Graphics, 2005

We present a novel approach for interactive view-dependent rendering of massive models. Our algorithm combines viewdependent simplification, occlusion culling, and out-of-core rendering. We represent the model as a clustered hierarchy of progressive meshes (CHPM). We use the cluster hierarchy for coarse-grained selective refinement and progressive meshes for fine-grained local refinement. We present an out-of-core algorithm for computation of a CHPM that includes cluster decomposition, hierarchy generation, and simplification. We introduce novel cluster dependencies in the preprocess to generate crack-free, drastic simplifications at runtime. The clusters are used for LOD selection, occlusion culling, and out-of-core rendering. We add a frame of latency to the rendering pipeline to fetch newly visible clusters from the disk and avoid stalls. The CHPM reduces the refinement cost of view-dependent rendering by more than an order of magnitude as compared to a vertex hierarchy. We have implemented our algorithm on a desktop PC. We can render massive CAD, isosurface, and scanned models, consisting of tens or a few hundred million triangles at 15-35 frames per second with little loss in image quality.

IEEE Transactions on …, 2003

Very large triangle meshes, i.e. meshes composed of millions of faces, are becoming common in many applications. Obviously, processing, rendering, transmission and archival of these meshes are not simple tasks. Mesh simplification and LOD management are a rather mature technology that in many cases can efficiently manage complex data. But only few available systems can manage meshes characterized by a huge size: RAM size is often a severe bottleneck. In this paper we present a data structure called Octreebased External Memory Mesh (OEMM ). It supports external memory management of complex meshes, loading dynamically in main memory only the selected sections and preserving data consistency during local updates. The functionalities implemented on this data structure (simplification, detail preservation, mesh editing, visualization and inspection) can be applied to huge triangles meshes on lowcost PC platforms. The time overhead due to the external memory management is affordable. Results of the test of our system on complex meshes are presented.

2003

In this paper we show how out-of-core mesh processing techniques can be adapted to perform their computations based on the new processing sequence paradigm, using mesh simplification as an example. We believe that this processing concept will also prove useful for other tasks, such as parameterization, remeshing, or smoothing, for which currently only in-core solutions exist. A processing sequence represents a mesh as a particular interleaved ordering of indexed triangles and vertices. This representation allows streaming very large meshes through main memory while maintaining information about the visitation status of edges and vertices. At any time, only a small portion of the mesh is kept in-core, with the bulk of the mesh data residing on disk. Mesh access is restricted to a fixed traversal order, but full connectivity and geometry information is available for the active elements of the traversal. This provides seamless and highly efficient out-of-core access to very large meshes for algorithms that can adapt their computations to this fixed ordering. The two abstractions that are naturally supported by this representation are boundary-based and buffer-based processing. We illustrate both abstractions by adapting two different simplification methods to perform their computation using a prototype of our mesh processing sequence API. Both algorithms benefit from using processing sequences in terms of improved quality, more efficient execution, and smaller memory footprints.

IEEE Transactions on Visualization and …, 2002

This paper describes a general framework for out-of-core rendering and management of massive terrain surfaces. The two key components of this framework are: view-dependent refinement of the terrain mesh; and a simple scheme for organizing the terrain data to improve coherence and reduce the number of paging events from external storage to main memory. Similar to several previously proposed methods for view-dependent refinement, we recursively subdivide a triangle mesh defined over regularly gridded data using longest-edge bisection. As part of this single, per-frame refinement pass, we perform triangle stripping, view frustum culling, and smooth blending of geometry using geomorphing. Meanwhile, our refinement framework supports a large class of error metrics, is highly competitive in terms of rendering performance, and is surprisingly simple to implement. Independent of our refinement algorithm, we also describe several data layout techniques for providing coherent access to the terrain data. By reordering the data in a manner that is more consistent with our recursive access pattern, we show that visualization of gigabyte-size data sets can be realized even on low-end, commodity PCs without the need for complicated and explicit data paging techniques. Rather, by virtue of dramatic improvements in multilevel cache coherence, we rely on the built-in paging mechanisms of the operating system to perform this task. The end result is a straightforward, simple-to-implement, pointerless indexing scheme that dramatically improves the data locality and paging performance over conventional matrix-based layouts.

in the hierarchy care has to be taken to prevent cracks along the cuts. This either leads to severe simplification constraints at the cuts and thus to a significantly higher number of triangles or the need for a costly runtime stitching of these nodes. In this paper we present an out-of-core visualization algorithm that overcomes this problem by filling the cracks generated by the simplification algorithm with appropriately shaded Fat Borders. Furthermore, several minor yet important improvements of previous approaches are made. This way we come up with a simple nevertheless ecient view- dependent rendering technique which allows for the natural incorporation of state-of-the- art culling, simplification, compression and prefetching techniques leading to real-time rendering performance of the overall system. Several examples demonstrate the eciency of our approach.

Computer Graphics Forum, 2012

The constantly increasing complexity of polygonal models in interactive applications poses two major problems. First, the number of primitives that can be rendered at real-time frame rates is currently limited to a few million. Secondly, less than 45 million triangles-with vertices and normal-can be stored per gigabyte. Although the rendering time can be reduced using level-of-detail (LOD) algorithms, representing a model at different complexity levels, these often even increase memory consumption. Out-of-core algorithms solve this problem by transferring the data currently required for rendering from external devices. Compression techniques are commonly used because of the limited bandwidth. The main problem of compression and decompression algorithms is the only coarse-grained random access. A similar problem occurs in view-dependent LOD techniques. Because of the interdependency of split operations, the adaption rate is reduced leading to visible popping artefacts during fast movements. In this paper, we propose a novel algorithm for real-time view-dependent rendering of gigabyte-sized models. It is based on a neighbourhood dependency-free progressive mesh data structure. Using a per operation compression method, it is suitable for parallel random-access decompression and out-of-core memory management without storing decompressed data.

The complexity of polygonal models is growing faster than the ability of graphics hardware to render them in real-time. If a scene contains many models and textures, it is often also not possible to store the entire geometry in the graphics memory. A common way to deal with such models is to use multiple levels of detail (LODs), which represent a model at different complexity levels. With view-dependent progressive meshes it is possible to render complex models in real time, but the whole progressive model must fit into graphics memory. To solve this problem out-of-core algorithms have to be used to load mesh data from external data devices. Hierarchical level of detail (HLOD) algorithms are a common solution for this problem, but they have numerous disadvantages. In this paper, we combine the advantages of view-dependent progressive meshes and HLODs by proposing a new algorithm for real-time view-dependent rendering of huge models. Using a spatial hierarchy we extend parallel view-dependent progressive meshes to support out-of-core rendering. In addition we present a compact data structure for progressive meshes, optimized for parallel GPU-processing and out-of-core memory management.

International Journal of Modelling and Simulation, 2005

Journal of Information Science and Engineering, 2006

Traditional iterative contraction based polygonal mesh simplification (PMS) algo- rithms usually require enormous amounts of main memory cost in processing large meshes. On the other hand, fast out-of-core algorithms based on the grid re-sampling scheme usually produce low quality output. In this paper, we propose a novel cache- based approach to large polygonal mesh simplification. The new approach introduces the