CLC number: TP391
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2021-06-08
Cited: 0
Clicked: 5687
Dening Luo, Yi Lin, Jianwei Zhang. GPU-based multi-slice per pass algorithm in interactive volume illumination rendering[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(8): 1092-1103.
@article{title="GPU-based multi-slice per pass algorithm in interactive volume illumination rendering",
author="Dening Luo, Yi Lin, Jianwei Zhang",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="22",
number="8",
pages="1092-1103",
year="2021",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2000214"
}
%0 Journal Article
%T GPU-based multi-slice per pass algorithm in interactive volume illumination rendering
%A Dening Luo
%A Yi Lin
%A Jianwei Zhang
%J Frontiers of Information Technology & Electronic Engineering
%V 22
%N 8
%P 1092-1103
%@ 2095-9184
%D 2021
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2000214
TY - JOUR
T1 - GPU-based multi-slice per pass algorithm in interactive volume illumination rendering
A1 - Dening Luo
A1 - Yi Lin
A1 - Jianwei Zhang
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 22
IS - 8
SP - 1092
EP - 1103
%@ 2095-9184
Y1 - 2021
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2000214
Abstract: volume rendering plays a significant role in medical imaging and engineering applications. To obtain an improved three-dimensional shape perception of volumetric datasets, realistic volume illumination has been considerably studied in recent years. However, the calculation overhead associated with interactive volume rendering is unusually high, and the solvability of the problem is adversely affected when the data size and algorithm complexity are increased. In this study, a scalable and GPU-based multi-slice per pass (MSPP) volume rendering algorithm is proposed which can quickly generate global volume shadow and achieve a translucent effect based on the transfer function, so as to improve perception of the shape and depth of volumetric datasets. In our real-world data tests, MSPP significantly outperforms some complex volume shadow algorithms without losing the illumination effects, for example, half-angle slicing. Furthermore, the MSPP can be easily integrated into the parallel rendering frameworks based on sort-first or sort-last algorithms to accelerate volume rendering. In addition, its scalable slice-based volume rendering framework can be combined with several traditional volume rendering frameworks.
[1]Angelelli P, Bruckner S, 2015. Performance and quality analysis of convolution-based volume illumination. J WSCG, 23(2):131-138.
[2]Belyaev SY, Smirnova ND, Smirnov PO, et al., 2019. Fast selective antialiasing for direct volume rendering. Proc SPIE, Medical Imaging: Imaging Informatics for Healthcare, Research, and Applications, 1095407.
[3]Beyer J, Hadwiger M, Pfister H, 2015. State-of-the-art in GPU-based large-scale volume visualization. Comput Graph Forum, 34(8):13-37.
[4]Bordoloi UD, Shen HW, 2005. View selection for volume rendering. Proc IEEE Visualization, p.487-494.
[5]Çalışkan A, Çevik U, 2015. Overview of computer graphics and algorithms. Proc 23rd Signal Processing and Communications Applications Conf, p.831-834.
[6]El Seoud MSA, Mady AS, 2019. A comprehensive review on volume rendering techniques. Proc 8th Int Conf on Software and Information Engineering, p.126-131.
[7]Engel K, Kraus M, Ertl T, 2001. High-quality pre-integrated volume rendering using hardware-accelerated pixel shading. Proc ACM SIGGRAPH/EUROGRAPHICS Workshop on Graphics Hardware, p.9-16.
[8]Fernando R, 2004. GPU Gems: Programming Techniques, Tips, and Tricks for Real-Time Graphics. Addison-Wesley, Boston, USA.
[9]Hachaj T, 2014. Real time exploration and management of large medical volumetric datasets on small mobile devices—evaluation of remote volume rendering approach. Int J Inform Manag, 34(3):336-343.
[10]Hänel C, Weyers B, Hentschel B, et al., 2016. Visual quality adjustment for volume rendering in a head-tracked virtual environment. IEEE Trans Vis Comput Graph, 22(4):1472-1481.
[11]Jönsson D, Sundén E, Ynnerman A, et al., 2014. A survey of volumetric illumination techniques for interactive volume rendering. Comput Graph Forum, 33(1):27-51.
[12]Khan NM, Ksantini R, Guan L, 2018. A novel image-centric approach toward direct volume rendering. ACM Trans Intell Syst Technol, 9(4):42.
[13]Kniss J, Premoze S, Hansen C, et al., 2002. Interactive translucent volume rendering and procedural modeling. Proc IEEE Visualization, p.109-116.
[14]Kruger J, Westermann R, 2003. Acceleration techniques for GPU-based volume rendering. Proc IEEE Visualization, p.287-292.
[15]Lacroute P, Levoy M, 1994. Fast volume rendering using a shear-warp factorization of the viewing transformation. Proc 21st Annual Conf on Computer Graphics and Interactive Techniques, p.451-458.
[16]Laur D, Hanrahan P, 1991. Hierarchical splatting: a progressive refinement algorithm for volume rendering. ACM SIGGRAPH Comput Graph, p.285-288.
[17]Ljung P, Krüger J, Groller E, et al., 2016. State of the art in transfer functions for direct volume rendering. Comput Graph Forum, 35(3):669-691.
[18]Ma B, Entezari A, 2018. Volumetric feature-based classification and visibility analysis for transfer function design. IEEE Trans Vis Comput Graph, 24(12):3253-3267.
[19]Mwalongo F, Krone M, Reina G, et al., 2016. State-of-the-art report in web-based visualization. Comput Graph Forum, 35(3):553-575.
[20]Noguera JM, Jimenez JR, 2016. Mobile volume rendering: past, present, and future. IEEE Trans Vis Comput Graph, 22(2):1164-1178.
[21]Patel D, Šoltészová V, Nordbotten JM, et al., 2013. Instant convolution shadows for volumetric detail mapping. ACM Trans Graph, 32(5):154.
[22]Preim B, Baer A, Cunningham D, et al., 2016. A survey of perceptually motivated 3D visualization of medical image data. Comput Graph Forum, 35(3):501-525.
[23]Rodríguez MB, Alcocer PPV, 2012. Practical volume rendering in mobile devices. Proc 8th Int Symp on Advances in Visual Computing, p.708-718.
[24]Rostamzadeh N, Jönsson D, Ropinski T, 2013. A comparison of volumetric illumination methods by considering their underlying mathematical models. Proc SIGRAD, Visual Computing, p.35-40.
[25]Schlegel P, Makhinya M, Pajarola R, 2011. Extinction-based shading and illumination in GPU volume ray-casting. IEEE Trans Vis Comput Graph, 17(12):1795-1802.
[26]Schott M, Pegoraro V, Hansen C, et al., 2009. A directional occlusion shading model for interactive direct volume rendering. Comput Graph Forum, 28(3):855-862.
[27]Šoltészová V, Patel D, Bruckner S, et al., 2010. A multidirectional occlusion shading model for direct volume rendering. Comput Graph Forum, 29(3):883-891.
[28]Stegmaier S, Strengert M, Klein T, 2005. A simple and flexible volume rendering framework for graphics-hardware-based raycasting. Proc 4th Int Workshop on Volume Graphics, p.187-195.
[29]Usher W, Amstutz J, Brownlee C, et al., 2017. Progressive CPU volume rendering with sample accumulation. Proc 17th Eurographics Symp on Parallel Graphics and Visualization, p.21-30.
[30]Wangkaoom K, Ratanaworabhan P, Thongvigitmanee SS, 2015. High-quality web-based volume rendering in real-time. Proc 12th Int Conf on Electrical Engineering/ Electronics, Computer, Telecommunications and Information Technology, p.1-6.
[31]Zhang Q, Eagleson R, Peters TM, 2011. Volume visualization: a technical overview with a focus on medical applications. J Dig Imag, 24(4):640-664.
[32]Zhang YB, Ma KL, 2013. Lighting design for globally illuminated volume rendering. IEEE Trans Vis Comput Graph, 19(12):2946-2955.
Open peer comments: Debate/Discuss/Question/Opinion
<1>