Irregular Volume Data Research Articles

Improvement of particle-based volume rendering for visualizing irregular volume data sets

We present a technique for previewing large-scale irregular volume datasets using an improved particle-based volume rendering (PBVR) technique. Volume rendering of irregular grid volume data is considerably more complicated than that of regular grid data, since the sampling and compositing processes, which must be done in visibility order, are not straightforward. In our original PBVR, rendering involves generating and projecting sub-pixel-size, opaque, and emissive particles without visibility ordering. To make it easier to preview large-scale irregular volume datasets, we improve our original PBVR technique in two respects. The first is that we exploit its scalability to develop a cell-by-cell particle generation technique. The second is that we reduce the memory cost of the frame buffer using a pixel-by-pixel superimposing technique. To measure the effectiveness of our proposed method, we apply it to huge irregular volume datasets composed of 71 mega hexahedral cells or 1 giga tetrahedral cells.

Particle-based multiple irregular volume rendering on CUDA

In this paper, we describe an improved particle-based volume rendering (PBVR) technique for previewing a large irregular volume dataset using the CUDA architecture. This technique allows for opaque and emissive particles to render translucent volumes without visibility sorting. Our GPU acceleration of PBVR provides the multi-volume rendering feature while remaining compatible with both regular and irregular volumes. We also reduce the memory cost required for storing all sub-pixel values by proposing a pixel repetition technique for a large sub-pixel level. By adjusting the repetition level, we achieved a very smooth level of detail (LOD) control for trading quality for speed. Our work demonstrates a full-detail rendering rate from 5 to 10 fps for irregular volume data with mega-scale cell numbers on an NVIDIA GeForce 8800GTS.

非一様な格子点分布データに対するボリュームMPU法による補間と圧縮

非一様な格子点分布データに対するボリュームMPU法による補間と圧縮

Volume MLS Ray Casting

The method of Moving Least Squares (MLS) is a popular framework for reconstructing continuous functions from scattered data due to its rich mathematical properties and well-understood theoretical foundations. This paper applies MLS to volume rendering, providing a unified mathematical framework for ray casting of scalar data stored over regular as well as irregular grids. We use the MLS reconstruction to render smooth isosurfaces and to compute accurate derivatives for high-quality shading effects. We also present a novel, adaptive preintegration scheme to improve the efficiency of the ray casting algorithm by reducing the overall number of function evaluations, and an efficient implementation of our framework exploiting modern graphics hardware. The resulting system enables high-quality volume integration and shaded isosurface rendering for regular and irregular volume data.

Integration of volume decompression and out-of-core iso-surface extraction from irregular volume data

Volume datasets tend to grow larger and larger as modern technology advances, thus imposing a storage constraint on most systems. One general solution to alleviate this problem is to apply volume compression on volume datasets. However, as volume rendering is often the most important reason why a volume dataset was generated in the first place, we must take into account how a volume dataset could be efficiently rendered when it is stored in a compressed form. Our previous work [21] has shown that it is possible to perform an on-the-fly direct volume rendering from irregular volume data. In this paper, we further extend that work to demonstrate that a similar integration can also be achieved on iso-surface extraction and volume decompression for irregular volume data. In particular, our work involves a dataset decomposition process, where instead of a coordinate-based decomposition used by conventional out-of-core iso-surface extraction algorithms, we choose to use a layer-based structure. Each such layer contains a collection of tetrahedra whose associated scalar values fall within a specific range, and can be compressed independently to reduce its storage requirement. The layer structure is particularly suitable for out-of-core iso-surface extraction, where the required memory exceeds the physical memory capacity of the machine that the process is running on. Furthermore, with this work, we can perform on-the-fly iso-surface extraction during decompression, and the computation only involves the layer that contains the query value, rather than the entire dataset. Experiments show that our approach can improve the performance up to ten times when compared with the results based on traditional coordinate-based approaches.

Avoiding errors in progressive tetrahedralizations

This paper describes some fundamental issues for robust implementations of progressively refined tetrahedralizations generated through sequences of edge collapses. We address the definition of appropriate cost functions and explain on various tests which are necessary to preserve the consistency of the mesh when collapsing edges. Although being considered a special case of progressive simplicial complexes [4], the results of our method are of high practical importance and can be used in many different applications, such as finite element meshing, scattered data interpolation or rendering of irregular volume data. CR Categories: I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling – surfaces and object representations.