Texture-based volume rendering of adaptive mesh refinement data

Many phenomena in nature and engineering happen simultaneously on rather diverse spatial and temporal scales. In other words, they exhibit a multi-scale character. A special numerical multilevel technique associated with a particular hierarchical data structure is adaptive mesh refinement (AMR). This scheme achieves locally very high spatial and temporal resolutions. Due to its popularity, many scientists are in need of interactive visualization tools for AMR data. In this article, we present a 3D texture-based volume-rendering algorithm for AMR data that directly utilizes the hierarchical structure. Thereby fast rendering performance is achieved even for high-resolution data sets. To avoid multiple rendering of regions that are covered by grids of different levels of resolution, we propose a space partitioning scheme to decompose the volume into axis-aligned regions of equal-sized cells. Furthermore the problems of interpolation artifacts, opacity corrections, and texture memory limitations are addressed. Published online: November 6, 2002 Correspondence to: R. K?hler     

GPU加速的八叉树体绘制算法

提出一种针对物体空间为序体绘制的空域跳过算法：采用双层次空间跳过,先以规则的数据分块作粗略地跳过,再以八叉树获得更高粒度的优化。该方法进一步解决了超过可用纹理内存容量的大规模体数据实时绘制问题,允许实时改变传递函数。针对该算法引入的CPU高负载瓶颈,提出一种新算法,在图形处理器(GPU)内快速计算采样面片,平衡了CPU与GPU间的运算负载。结合上述两种算法,实现高效的大规模体数据绘制并无损图像质量。     

A single-pass GPU ray casting framework for interactive out-of-core rendering of massive volumetric datasets

We present an adaptive out-of-core technique for rendering massive scalar volumes employing single-pass GPU ray casting. The method is based on the decomposition of a volumetric dataset into small cubical bricks, which are then organized into an octree structure maintained out-of-core. The octree contains the original data at the leaves, and a filtered representation of children at inner nodes. At runtime an adaptive loader, executing on the CPU, updates a view and transfer function-dependent working set of bricks maintained on GPU memory by asynchronously fetching data from the out-of-core octree representation. At each frame, a compact indexing structure, which spatially organizes the current working set into an octree hierarchy, is encoded in a small texture. This data structure is then exploited by an efficient stackless ray casting algorithm, which computes the volume rendering integral by visiting non-empty bricks in front-to-back order and adapting sampling density to brick resolution. Block visibility information is fed back to the loader to avoid refinement and data loading of occluded zones. The resulting method is able to interactively explore multi-gigavoxel datasets on a desktop PC.     

Interactive volume rendering of large sparse data sets using adaptive mesh refinement hierarchies

In this paper, we present an algorithm that accelerates 3D texture-based volume rendering of large, sparse data sets, i.e., data sets where only a traction of the voxels contain relevant information. In texture-based approaches, the rendering performance is affected by the fill-rate, the size of texture memory, and the texture I/O bandwidth. For sparse data, these limitations can be circumvented by restricting most of the rendering work to the relevant parts of the volume. In order to efficiently enclose the corresponding regions with axis-aligned boxes, we employ a hierarchical data structure, known as an AMR (adaptive mesh refinement) tree. The hierarchy is generated utilizing a clustering algorithm. A good balance is thereby achieved between the size of the enclosed volume, i.e., the amount to render in graphics hardware and the number of axis-aligned regions, i.e., the number of texture coordinates to compute in software. The waste of texture memory by the power-of-two restriction is minimized by a 3D packing algorithm which arranges texture bricks economically in memory. Compared to an octree approach, the rendering performance is significantly increased and less parameter tuning is necessary.     

Incremental Volume Rendering Using Hierarchical Compression

We present a new algorithm here for efficient incremental rendering of volumetric datasets. The primary goal of this algorithm is to give average workstations the ability to efficiently render volume data received over relatively low bandwidth network links in such a way that rapid user feedback is maintained. Common limitations of workstation rendering of volume data include: large memory overheads, the requirement of expensive rendering hardware, and high speed processing ability. The rendering algorithm presented here overcomes these problems by making use of the efficient Shear-Warp Factorisation method which does not require specialised graphics hardware. However the original Shear-Warp algorithm suffers from a high memory overhead and does not provide for incremental rendering which is required should rapid user feedback be maintained. Our algorithm represents the volumetric data using a hierarchical data structure which provides for the incremental classification and rendering of volume data. This exploits the multiscale nature of the octree data structure. The algorithm reduces the memory footprint of the original Shear-Warp Factorisation algorithm by a factor of more than two, while maintaining good rendering performance. These factors make our octree algorithm more suitable for implementation on average desktop workstations for the purposes of interactive exploration of volume models over a network. Results from tests using typical volume datasets will be presented which demonstrate the ability of the algorithm to achieve high rendering rates for both incremental rendering and standard rendering while reducing the runtime memory requirements.     

基于打包索引纹理的大规模数据体绘制算法

针对大规模数据体绘制效率低下的问题,提出一种算法：对体数据进行纹理分块打包,移除空数据块,并创建数据块的索引数据,绘制时通过索引访问打包后的纹理实现大规模数据完全载入显存,同时在索引中标记空数据及高密度数据块的位置,绘制前生成其有效的立方体数据表达,结合早期光线终止与空域跳过等加速技术,有效地实现了大规模的体数据的实时绘制,同时保证了结果图像的质量。     

基于自适应八叉树划分的高精度四面体可视化

利用八叉树结构将四面体数据转化为规则网格数据,能有效提高系统的交互性能.八叉树的划分层次越高,绘制效果越好,但数据的存储空间以及处理时间也将大幅增多.提出自适应的规则化表示方法来构建八叉树结构,改进原有的单一采样策略,并结合深度信息将采样结果转换成适用于GPU的八叉树纹理结构.然后采用光线投射算法来对体数据进行绘制,根据各区域深度不一的特点,提出了变步长的采样绘制策略.实验结果表明,本文方法降低了数据的空间存储量和处理时间,同时在绘制质量、绘制效率方面都得到了较大提高.     

Distributed shared memory for roaming large volumes

We present a cluster-based volume rendering system for roaming very large volumes. This system allows to move a gigabyte-sized probe inside a total volume of several tens or hundreds of gigabytes in real-time. While the size of the probe is limited by the total amount of texture memory on the cluster, the size of the total data set has no theoretical limit. The cluster is used as a distributed graphics processing unit that both aggregates graphics power and graphics memory. A hardware-accelerated volume renderer runs in parallel on the cluster nodes and the final image compositing is implemented using a pipelined sort-last rendering algorithm. Meanwhile, volume bricking and volume paging allow efficient data caching. On each rendering node, a distributed hierarchical cache system implements a global software-based distributed shared memory on the cluster. In case of a cache miss, this system first checks page residency on the other cluster nodes instead of directly accessing local disks. Using two Gigabit Ethernet network interfaces per node, we accelerate data fetching by a factor of 4 compared to directly accessing local disks. The system also implements asynchronous disk access and texture loading, which makes it possible to overlap data loading, volume slicing and rendering for optimal volume roaming.     

针对全空子数据体的GPU体绘制

目的 体绘制是3维数据可视化的主要方法之一。用于体绘制的数据体中包含有大量的空体素,导致光线投射算法进行没有意义的重采样计算,必然降低绘制算法效率。针对全空子数据体体绘制低效问题,提出基于GPU体高效绘制方法。方法 利用八叉树数据结构组织数据,有效管理包含许多空体素的子数据体。通过绘制八叉树非全空叶子节点子数据体表面,使光线投射算法中起始和终止重采样位置更接近数据体中的可视部分,同时根据八叉树全空节点子数据体判定纹理查询结果,计算合适的跳跃步长,快速跳过八叉树中全空节点子数据体。结果 当数据体中空体素较多时,确定合适的八叉树深度,有效地跳过数据体中的空体素,减少体绘制运算量,实现对原基于体包围盒表面绘制的GPU光线投射算法的加速。结论 设计不透明度函数,凸显数据体中层位面,并将算法成功应用于地震数据可视化,取得很好应用效果。     

NeTra: A toolbox for navigating large image databases

We present here an implementation of NeTra, a prototype image retrieval system that uses color, texture, shape and spatial location information in segmented image regions to search and retrieve similar regions from the database. A distinguishing aspect of this system is its incorporation of a robust automated image segmentation algorithm that allows object- or region-based search. Image segmentation significantly improves the quality of image retrieval when images contain multiple complex objects. Images are segmented into homogeneous regions at the time of ingest into the database, and image attributes that represent each of these regions are computed. In addition to image segmentation, other important components of the system include an efficient color representation, and indexing of color, texture, and shape features for fast search and retrieval. This representation allows the user to compose interesting queries such as “retrieve all images that contain regions that have the color of object A, texture of object B, shape of object C, and lie in the upper of the image”, where the individual objects could be regions belonging to different images. A Java-based web implementation of NeTra is available at http://vivaldi.ece.ucsb.edu/Netra.