• Journal of the Korea Computer Graphics Society
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• v.11 no.1
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• pp.40-49
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• 2005

Several optimization techniques have been proposed for volume ray casting, but these cannot achieve real-time frame rates. In addition, it is difficult to apply them to some applications that require perspective projection. Recently, hardware-based methods using 3D texture mapping are being used for real-time volume rendering. Although rendering speed approaches real time, the larger volumes require more swapping of volume bricks for the limited texture memory. Also, image quality deteriorates compared with that of conventional volume ray casting. In this paper, we propose a data structure for real-time volume ray casting named PERM (Precomputed dEnsity and gRadient Map). The PERM stores interpolated density and gradient vector for quantized cells. Since the information requiring time-consuming computations is stored in the PERM, our method can ensure interactive frame rates on a consumer PC platform. Our method normally produces high-quality images because it is based on conventional volume ray casting.

• Journal of Biomedical Engineering Research
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• v.26 no.1
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• pp.23-30
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• 2005

3D virtual endoscopy has been used as an alternative non-invasive procedure for visualization of hollow organs. However, due to computational complexity, this is a time-consuming procedure. In this paper, we propose a fast volume rendering algorithm based on perspective ray casting for virtual endoscopy. As a pre-processing step, the algorithm divides a volume into hierarchical blocks and classifies them into opaque or transparent blocks. Then, in the first step, we perform ray casting only for sub-sampled pixels on the image plane, and determine their pixel values and depth information. In the next step, by reducing the sub-sampling factor by half, we repeat ray casting for newly added pixels, and their pixel values and depth information are determined. Here, the previously obtained depth information is utilized to reduce the processing time. This step is recursively performed until a full-size rendering image is acquired. Experiments conducted on a PC show that the proposed algorithm can reduce the rendering time by 70- 80% for bronchus and colon endoscopy, compared with the brute-force ray casting scheme. Using the proposed algorithm, interactive volume rendering becomes more realizable in a PC environment without any specific hardware.

• ETRI Journal
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• v.42 no.4
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• pp.608-618
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• 2020

We present a novel GPU-based ray-casting algorithm for volume rendering of unstructured grid data. Our volume rendering system uses a ray-casting method that guarantees accurate rendering results. We also employ the per-pixel intersection list concept in the Bunyk algorithm to guarantee an accurate result for non-convex meshes. For efficient memory access for the lists on the GPU, we represent the intersection lists for all faces as an array with our novel construction algorithm. With the intersection lists, we perform ray-casting on a GPU, and a GPU thread handles each ray. To increase ray-coherency in a thread block and improve memory access efficiency, we extend a prior image-tile-based work distribution method to fit modern GPU architectures. We also show that a prior approach using a per-thread local buffer to reduce redundant computation is not appropriate for modern GPU architectures. Instead, we take an on-demand calculation strategy that achieves better performance even though it allows duplicate computations. We applied our method to three unstructured grid datasets with different characteristics. With a GPU, our method achieved up to 36.5 times higher performance for the ray-casting process and 19.7 times higher performance for the whole volume rendering process compared with the Bunyk algorithm using a CPU core. Also, our approach showed up to 8.2 times higher performance than a GPU-based cell projection method while generating more accurate rendering results. These results demonstrate the efficiency and accuracy of our method.

• Journal of Korea Society of Industrial Information Systems
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• v.9 no.2
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• pp.23-31
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• 2004

This paper focuses on a boundary surface based ray casting. In general the boundary surface based ray casting is processed in two stages. The first stage finds boundary surfaces and stores them into buffers. The second stage calculates a distance from a viewpoint to the voxels of the interested area by projecting boundary surfaces on the view plane, and then starts to traverse a volume data space with the distance. Our approach differs from the general boundary surface based ray casting in processing the first stage of it. Contrast to the typical boundary surface based ray casting where all boundary surfaces of volume data are stored into buffers, they are projected on the planes aligned to the axis of volume data coordinates and these projected data are stored into 6 buffers. Such maneuver shortens time for ray casting, and reduces memory usage because it can be carried out independently from the amount of the volume data.

• Journal of KIISE:Computer Systems and Theory
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• v.36 no.3
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• pp.210-216
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• 2009

Several empty-space leaping methods have been proposed for CPU-based volume ray casting. When sample points are located in semi-transparent cells, however, previous leaping methods perform unnecessary resamplings even if the scalar values on those points are confined within transparent range. A semi-transparent cells leaping method for volume ray casting using the Marching Cubes algorithm is proposed to solve this problem in our previous work. When a ray reaches a semi-transparent cell, our method performs in-out test between current sample point and the bounding box enclosing the triangles generated by the Marching Cubes. If the sample point lies on outside of the bounding box, we estimate the point is regarded as transparent. In this case, the ray advances to the next sample point without performing a resampling operation. We can frequently refer the tables for neighboring voxels, however, when we exploit conventional data structures of the Marching Cubes. We propose modified Marching Cubes tables for solving this problem.

• Journal of Korea Game Society
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• v.12 no.2
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• pp.53-62
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• 2012

Visualizing a volume dataset with ray-casting which of visualization methods provides high quality image. However it spends too much time for rendering because the size of volume data are huge. Recently, various researches have been proposed to accelerate GPU-based volume rendering to solve these problems. In this paper, we propose an efficient GPU-based empty space skipping to accelerate volume ray-casting using octree traversal. This method creates min-max octree and searches empty space using vertex splitting. It minimizes the bounding polyhedron by eliminating empty space found in the octree traveral step. The rendering results of our method are identical to those of previous GPU-based volume ray-casting, with the advantage of faster run-time because of using minimized bounding polyhedron.

• Journal of KIISE
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• v.43 no.3
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• pp.304-313
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• 2016

We present a novel memory-efficient parallel ray casting algorithm for unstructured grid volume rendering on multi-core CPUs. Our method is based on the Bunyk ray casting algorithm. To solve the high memory overhead problem of the Bunyk algorithm, we allocate a fixed size local buffer for each thread and the local buffers contain information of recently visited faces. The stored information is used by other rays or replaced by other face's information. To improve the utilization of local buffers, we propose an image-plane based ray grouping algorithm that makes ray groups have high coherency. The ray groups are then distributed to computing threads and each thread processes the given groups independently. We also propose a novel hash function that uses the index of faces as keys for calculating the buffer index each face will use to store the information. To see the benefits of our method, we applied it to three unstructured grid datasets with different sizes and measured the performance. We found that our method requires just 6% of the memory space compared with the Bunyk algorithm for storing face information. Also it shows compatible performance with the Bunyk algorithm even though it uses less memory. In addition, our method achieves up to 22% higher performance for a large-scale unstructured grid dataset with less memory than Bunyk algorithm. These results show the robustness and efficiency of our method and it demonstrates that our method is suitable to volume rendering for a large-scale unstructured grid dataset.

• Journal of Biomedical Engineering Research
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• v.22 no.4
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• pp.339-347
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• 2001

• Korean Journal of Computational Design and Engineering
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• v.16 no.4
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• pp.285-291
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• 2011

A pose estimation process from medical images is calculating locations and orientations of objects obtained from Computed Tomography (CT) volume data utilizing X-ray images from two directions. In this process, digitally reconstructed radiograph (DRR) images of spatially transformed objects are generated and compared to X-ray images repeatedly until reasonable transformation matrices of the objects are found. The DRR generation and image comparison take majority of the total time for this pose estimation. In this paper, a fast DRR generation technique based on GPU parallel computing is introduced. A volume ray-casting algorithm is explained with brief vector operations and a parallelization technique of the algorithm using Compute Unified Device Architecture (CUDA) is discussed. This paper also presents the implementation results and time measurements comparing to those from pure-CPU implementation and open source toolkit.

• Journal of Biomedical Engineering Research
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• v.21 no.5
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• pp.519-526
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• 2000

볼륨 렌더링은 3D 의료영상 데이터를 가시화하는 중용한 기법 중 하나이다. 그러나 볼륨 렌더링을 실시간으로 이룰 때, 많은 계산량을 필요로하는 것이 볼률 렌더링을 사용하는데 걸림돌이 되고 있다. 이 논문에서는 Superscalar와 VLIM(Very Long Instruction Word)의 구조를 가지고 있어 동시에 8개의 명령어 수행이 가능한 TI사의 TMS320C6201 DSP를 이용하여 3D 초음파 영상의 쉬어-웝 볼륨 렌더링을 구현하였다. 쉬어-웝 방법을 DSP 상에서 최적으로 구현하기 위하여 ray map 방법, one-to-four ray casting, ？디 skipping 방법을 제안하였다. 제안한 방법들을 이용한 볼륨 렌더링과 적용하지 않은 기존의 알고리즘을 DSP에 구현하여 PSNR과 렌더링 시간의 비교·평가를 통해 만족할 만한 영상 화질에 빠른 렌더링 성능을 얻을 수 있음을 보여주었다.