GPU-based object-order ray-casting for large datasets

Wei Hong, Feng Qiu, A. Kaufman
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引用次数: 38

Abstract

We propose a GPU-based object-order ray-casting algorithm for the rendering of large volumetric datasets, such as the Visible Human CT datasets. A volumetric dataset is decomposed into small sub-volumes, which are then organized using a min-max octree structure. The small sub-volumes are stored in the leaf nodes of the min-max octree, which are also called cells. The cells are classified using a transfer function, and the visible cells are then loaded into the video memory or the AGP memory. The cells are sorted and projected onto the image plane front to back. The cell projection is implemented using a volumetric ray-casting algorithm on the GPU. In order to make the cell projection more efficient, we devise a propagation method to sort cells into layers. The cells within the same layer are projected at the same time. We demonstrate the efficiency of our algorithm using the visible human datasets and a segmented photographic brain dataset on commodity PCs.
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基于gpu的大型数据集对象顺序光线投射
我们提出了一种基于gpu的物体顺序光线投射算法,用于绘制大型体积数据集,如可见人体CT数据集。体积数据集被分解成小的子卷,然后使用最小-最大八叉树结构组织这些子卷。小的子卷存储在最小最大八叉树的叶节点中,这些叶节点也称为细胞。使用传递函数对细胞进行分类,然后将可见细胞加载到视频存储器或AGP存储器中。对细胞进行排序,并将其前后投影到图像平面上。在GPU上使用体积光线投射算法实现单元投影。为了提高细胞投影的效率,我们设计了一种将细胞分层的繁殖方法。同一层内的细胞同时投影。我们使用商用pc上可见的人类数据集和分割的图像大脑数据集来证明我们的算法的效率。
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