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Adaptive Algorithm for Accelerating Direct Isosurface Rendering on GPU

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Adaptive Algorithm for Accelerating Direct Isosurface Rendering on GPU 224 JOURNAL OF ELECTRONIC SCIENCE AND TECHNOLOGY, VOL. 16, NO. 3, SEPTEMBER 2018 Digital Object Identifier:10.11989/JEST.1674-862X.71013102 Adaptive Algorithm for Accelerating Direct Isosurface Rendering on GPU Sergey Belyaev | Pavel Smirnov | Vladislav Shubnikov | Natalia Smirnova* Abstract—Direct isosurface volume rendering is the most prominent modern method for medical data visualization. It is based on finding intersection points between the rays corresponding to pixels on the screen and isosurface. This article describes a two-pass algorithm for accelerating the method on the graphic processing unit (GPU). On the first pass, the intersections with the isosurface are found only for a small number of rays, which is done by rendering into a lower-resolution texture. On the second pass, the obtained information is used to efficiently calculate the intersection points of all the other. The number of rays to use during the first pass is determined by using an adaptive algorithm, which runs on the central processing unit (CPU) in parallel with the second pass of the rendering. The proposed approach allows to significantly speed up isosurface visualization without quality loss. Experiments show acceleration up to 10 times in comparison with a common ray casting method implemented on GPU. To the authors’ knowledge, this is the fastest approach for ray casting which does not require any preprocessing and could be run on common GPUs. Index Terms—Adaptive algorithms, isosurface rendering, ray casting, volume visualization. 1. Introduction Volume rendering techniques are important for medical data visualization. They provide a crucial means for performing visual analysis of a patient’s anatomy for diagnosis, preoperative planning, and surgical training. Direct methods do not require intermediate representation generating. They include direct volume rendering and direct isosurface rendering. The first method evaluates the volume integral for all pixels of the final image; this method is very convenient for exploring 3-dimensional (3D) data sets. The second one visualizes an isosurface level selected by the user. It requires less computational power, since to visualize the isosurface, one only needs to find intersection points of the rays with it. This method is based on the algorithm called raycasting. Under this algorithm, the intersection is found by moving through the data set along a ray passing through a given pixel with some step length until a value exceeding the isosurface level is found. *Corresponding author Manuscript received 2017-10-13; revised 2017-11-21. S. Belyaev, P. Smirnov, V. Shubnikov, and N. Smirnova are with the Department of Applied Mathematics, Institute of Applied Mathematics and Mechanics, Peter the Great St. Petersburg Polytechnic University, St. Petersburg 195251, and also with EPAM Systems Inc., Newtown PA 18940 (e-mail: [email protected]; [email protected]; [email protected]; [email protected]). Color versions of one or more of the figures in this paper are available online at http://www.journal.uestc.edu.cn. Publishing editor: Yu-Lian He BELYAEV et al.: Adaptive Algorithm for Accelerating Direct Isosurface Rendering on GPU 225 Raycasting is usually accelerated using preprocessing, wherein auxiliary data structures are constructed for culling the empty blocks, shortening the distance traveled along rays, or varying the step length along the rays, during runtime. The proposed method, on the other hand, does not require preprocessing. It constructs the necessary auxiliary data on the first step of the rendering process for every frame. The less pixels the first step involves, the faster it is; however, if too few pixels are used, artifacts begin to appear. To find out the minimum required number of pixels needed to prevent the formation of artifacts, an adaptive algorithm is used that runs on the central processing unit (CPU) in parallel with the second step of the rendering process. 2. Related Works There are two approaches for visualizing 3D arrays. Some of them involve rasterizing the polygons obtained from the array, e.g. marching cubes[1]. A detailed overview and comparison of those algorithms were presented in [2]. The advantage of this approach is that polygonal models can be rendered very quickly. The disadvantage, however, is that the raw data has to be preprocessed before visualizing. This can be a problem if the transfer function changes or the raw data is to be plotted dynamically. Although there were works[3],[4] dedicated to fast triangulation on the graphic processing unit (GPU), the other approach—direct isosurface or volume rendering—is preferred in those cases. The most flexible and popular method for direct isosurface rendering is raycasting, which is also used in direct volume rendering. GPU-based raycasting was presented in [5]. It made use of the cube proxy geometry (the dataset’s bounding box) to determine the start and the end points of the ray. This method does not require preprocessing and can be used to visualize raw data directly. Unfortunately, it is very slow. For every pixel, it requires finding the intersection of the ray and isosurface by tracing the ray from start to end. A significant amount of time is wasted on processing rays that do not intersect the isosurface at all. In order to accelerate the finding of intersections and cull the rays that do not intersect the isosurface, most authors fall back on preprocessing. In [6], the isosurface was covered in a set of cuboids during preprocessing, which allowed one to cull the empty blocks during runtime and to reduce the ray’s length. In [7], these blocks were assembled into an octree structure. In [8], a min-max octree structure was introduced. Other variants of octree structures were examined in [7], [9] to [16]. An alternative structure that serves the same purpose is the kd-tree[17]-[19]. Recently, a lot of effort has been spent on reducing the preprocessing time by making use of new GPU functions[15]-[23]. These works use a histogram pyramid structure, an analog of the mipmap structure. A histogram pyramid lets one convert a 3D volume into a point cloud entirely on the graphics hardware[24]. The algorithm reduces a highly sparse matrix with N elements to a list of its M active entries in O(N+Mlog N) steps. It should be noted that this structure is also used to optimize triangulation on GPU[3]. The method that was presented in [25] is the peak-finding method, which accelerates direct volumetric rendering by determining the isosurfaces in advance. In this case, it is desirable to not only determine the ray’s starting point, but also all the points where it intersects the isosurface. Of the works examined above, this can be done with the methods from [22] and [23]. Other approaches to solving this problem were examined in [21] and [26]. In [21], the approach presented is the pre-integration approach[27],[28], in which it is the isosurfaces, and not the calculated integrals, that are saved. In [26], a triangular net is built during preprocessing, and is later rendered into an A-buffer[29]. The lists in the A-buffer were sorted by the distance to the viewer. Various implementations of the A-buffer on GPU using the K- and G- buffers can be found in [30]. 226 JOURNAL OF ELECTRONIC SCIENCE AND TECHNOLOGY, VOL. 16, NO. 3, SEPTEMBER 2018 Some of the methods listed above can be used efficiently for visualizing large amounts of data that does not fit fully into the GPU memory. An overview of processing big data sets can be found in [31]. The presented algorithm is independent of the input size. Another class of problems that use direct isosurface raycasting is multifield data visualization. Such data is very common in medicine, for instance that in [32]. From the viewpoint of raycasting, they are special in the regard that several isosurfaces need to be handled simultaneously. In [33], where fiber surfaces were visualized, triangular nets constructed during preprocessing were used for this purpose. Works dedicated to the acceleration of raycasting without using preprocessing data include [25], [34], and [35]. They used occlusion frustums as proxy geometry, obtained from a previously rendered frame. Unfortunately, this approach can handle only coherent changes in view parameters, and is unusable for cases where the transfer function changes rapidly or the input data changes completely. It also performs poorly on multifield data visualization, as it only registers the first intersection of a ray and an isosurface. An alternative approach to increasing the frame rate regardless of whether preprocessing is done was presented in [36] to [40]. They attempted to keep up the required frame rate by worsening the quality of the resulting image to the least extent possible. To do that, [36], [37], and [40] used analytical performance modeling (regression, genetic algorithms, and performance skeletons), whereas [38], [39], and [41] used machine learning approaches. 3. Algorithm Description The work in [5] outlines the idea of using a bounding box that bounds the given volume to determine the start and end points of rays. In Fig. 1, these rays are denoted with ri, and their start and end points are denoted with fi and li, respectively. The runtime of the raycasting algorithm depends on the step length along the rays and the number of rays. The main idea of the proposed algorithm is to reduce the number of rays. In accordance with it, the ray-isosurface intersections are found for a smaller number of rays and then obtained information is used to predict where the remaining rays will intersect the isosurface. The idea is implemented using a two-pass algorithm. On the first pass, the rendering is done into an auxiliary texture T, which has a resolution n times lower than the screen in both directions.
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