计算机科学 ›› 2022, Vol. 49 ›› Issue (1): 175-180.doi: 10.11896/jsjkx.210200042

• 计算机图形学&多媒体 • 上一篇    下一篇

动态低采样环境光遮蔽的实时光线追踪分子渲染

李家振, 纪庆革   

  1. 中山大学计算机学院 广州510006
    广东省大数据分析与处理重点实验室 广州510006
  • 收稿日期:2021-02-04 修回日期:2021-07-01 出版日期:2022-01-15 发布日期:2022-01-18
  • 通讯作者: 纪庆革(issjqg@mail.sysu.edu.cn)
  • 作者简介:lijzh66@mail2.sysu.edu.cn
  • 基金资助:
    国家自然科学基金联合基金(U1611263)

Dynamic Low-sampling Ambient Occlusion Real-time Ray Tracing for Molecular Rendering

LI Jia-zhen, JI Qing-ge   

  1. School of Computer Science and Engineering,Sun Yat-sen University,Guangzhou 510006,China
    Guangdong Province Key Laboratory of Big Data Analysis and Processing,Guangzhou 510006,China
  • Received:2021-02-04 Revised:2021-07-01 Online:2022-01-15 Published:2022-01-18
  • About author:LI Jia-zhen,born in 1997,postgraduate.His main research interests include computer graphic and virtual reality.
    JI Qing-ge,born in 1966,Ph.D,associate professor,is a member of China Computer Federation.His main research interests include computer graphic,vir-tual reality and computer vision.
  • Supported by:
    Natural Science Foundation of Jiangsu Province,China(BK20141209).

摘要: 分子可视化工作中高质量的分子渲染效果对研究人员观察生物分子结构尤为重要。主流分子可视化工具中常用的光栅化方法渲染效果不佳,不利于研究人员观察分子结构。先进的光线追踪渲染技术可以实现高质量的渲染效果,但目前工具中支持光线追踪的分子渲染方法存在使用平台限制、实时性能不足以及渲染质量不佳的问题。文中提出一种动态低采样环境光遮蔽的实时光线追踪分子渲染方法,其中提出了光线追踪中简易的重投影方法,用于实现动态低采样环境光遮蔽的时间性降噪;以及提出了阴影光线包装策略,改进了光线遍历场景时的计算并行度。实验结果表明,所提方法在个人电脑上可达到实时交互渲染性能,并且在“天河二号”平台上与先进的VMD-OSPRay方法相比,该方法获得了1.40~1.64倍的性能加速,同时改善了动态图像严重的噪点问题。

关键词: 光线追踪, 分子可视化, 分子渲染, 环境光遮蔽, 数据并行

Abstract: High-quality rendering in molecular visualization are particularly important for researchers to observe the structure of biomolecules.The rasterization rendering effect commonly used by mainstream molecular visualization tools is not good.Advanced ray tracing rendering technology can achieve high-quality rendering effects,however,molecular rendering methods that support ray tracing in the current tools have various problems such as platform limitations,insufficient real-performance,and poor rende-ring quality.In this paper,a dynamic low-sampling ambient occlusion real-time ray tracing for molecular rendering is proposed.A simple reprojection method for ray tracing is proposed to implement temporal denoising of low-sampling ambient occlusion in dynamic.We also propose a shadow rays packet strategy to improve the parallelism of calculation when the ray traverses the scene.Experimental results show that our method can achieve interactive rendering performance on PC,and compared with the advanced VMD-OSPRay method on the TH-2 supercomputer,our method achieves performance acceleration of 1.40 to 1.64 times,and improves the serious noise problem of dynamic images.

Key words: Ray tracing, Molecular visualization, Molecular rendering, Ambient occlusion, Data parallel

中图分类号: 

  • TP391
[1]MATTHEEWS N,EADSON R,KITAO A,et al.High quality rendering of protein dynamics in space filling mode[J].Journal of Molecular Graphics and Modelling,2017,78:158-167.
[2]SAYLE R,MILNER-WHITE J.RASMOL:biomolecular gra-phics for all[J].Trends in Biochemical Sciences,1995,20(9):374-376.
[3]DELANO L,WARREN L.PyMOL:An Open-Source Molecular Graphics Tool[J].CCP4 Newsletter on protein crystallography,2002,40(1):82-92.
[4]HUMPHREY W,DALKE A,SCHULTEN K.VMD:Visualmolecular dynamics[J].Journal of Molecular Graphics,1996,14(1):33-38.
[5]KOZLIKOVA B,KRONE M,FALK M,et al.Visualization ofBiomolecular Structures:State of Art Revisited[J].Computer Graphics Forum,2016,36(8):178-204.
[6]MARTINEZ X,CHAVENT M,BAADEN M.Visualizing protein structures—tools and trends[J].Biochemical Society Transactions,2020,48(2):499-506.
[7]KUCHKUDA R.An Introduction to Ray Tracing [M]//Theoretical Foundations of Computer Graphics and CAD.Berlin:Springer,1988,40:1039-1060.
[8]WALD I,KNOLL A,JOHNSON G,et al.CPU ray tracing large particle data with balanced P-k-d trees[C]//2015 IEEE Scienti-fic Visualization Conference.IEEE,2015:57-64.
[9]BITTNER J,HAPALA M,HAVRAN V.Incremental BVHconstruction for ray tracing[J].Computers & Graphics,2015,47(4):135-144.
[10]WALD I,SLUSALLEK P,BENTHIN C,et al.Interactive Rendering with Coherent Ray Tracing[J].Computer Graphics Forum,2001,20(13):153-165.
[11]BOULOS S,EDWARDS D,LACEWELL D,et al.Packet-based whitted and distribution ray tracing[C]//Proceedings of Grap-hics Interface 2007(GI'07).ACM,2007:177-184.
[12]BARRINGER R,AKENINE-MOLLER T.Dynamic Ray Stream Traversal[J].ACM Transactions on Graphics,2014,33(4):1-9.
[13]FRA A T,BENTHIN C,WALD I,et al.Local Shading Cohe-rence Extraction for SIMD-efficient Path Tracing on CPUs[C]//Proceedings of High Performance Graphics(HPG'16).Eurographics Association,2016:119-128.
[14]STONE J E.Interactive ray tracing techniques for high-fidelity scientific visualization[M]//Ray Tracing Gems.Apress,Berkeley,CA,2019:493-515.
[15]MARSALEK L,DEHOF A K,GEORGIEV I,et al.Real-timeRay Tracing of Complex Molecular Scenes[C]//14th International Conference Information Visualization.IEEE,2010:239-245.
[16]GEORGIEV I,SLUSALLEK P.RTfact:Generic concepts forflexible and high performance ray tracing[C]//IEEE Sympo-sium on Interactive Ray Tracing.IEEE,2008:115-122.
[17]STONE J,VANDIVORT K,SCHULTEN K.GPU-accelerated Molecular Visualization on Petascale Supercomputing Platforms[C]//In Proceedings of the 8th International Workshop on Ultrascale Visualization.ACM,2013.
[18]PARKER S,BIGLER J,DIETRICH A,et al.Optix:A General Purpose Ray Tracing Engine[J].ACM Transactions on Gra-phics,2010,29(4):1-13.
[19]KNOLL A,WALD I,PAPKA M,et al.Ray tracing and volume rendering large molecular data on multi-core and many-core architectures[C]//Proceedings of the 8th International Workshop on Ultrascale Visualization.ACM,2013:1-8.
[20]WALD I,JOHNSON G,AMSTUTZ J,et al.OSPRay-A CPU Ray Tracing Framework for Scientific Visualization[J].IEEE Transactions on Visualization and Computer Graphics,2016,23(1):931-940.
[21]WALD I,SLUSALLEK P,BENTHIN C,et al.Embree-A Ray Tracing Kernel Framework for Efficient CPU Ray Tracing[J].ACM Transactions on Graphics,2014,33(4):1-8.
[22]WANG F,WALD I,WU Q,et al.CPU isosurface ray tracing of adaptive mesh refinement data[J].IEEE Transactions on Visua-lization and Computer Graphics,2018,25(1):1142-1151.
[23]HAN M,WALD I,USHER W,et al.Ray tracing generalizedtube primitives:Method and applications[J].Computer Gra-phics Forum,2019,38(3):467-478.
[24]SUSSMAN J,ABOLA E,LIN D,et al.The Protein Data Bank[J].Genetica,1999,106(1):149-158.
[25]HERMOSILLA P,VAZQUEZ P,VINACUA A,et al.A Gene-ral Illumination Model for Molecular Visualization[J].Computer Graphics Forum,2018,37(3):367-378.
[26]KARIS B.High Quality Temporal Supersampling [R].Sig-graph,2014.
[27]ANDERSSON P,NILSSON J,SALVI M,et al.TemporallyDense Ray Tracing[C]//High Performance Graphics.2019:33-38.
[28]WISSMANN N,MISIAK M,FUHRMANN A,et al.Accele-rated Stereo Rendering with Hybrid Reprojection-Based Raste-rization and Adaptive Ray-Tracing[C]//2020 IEEE Conference on Virtual Reality and 3D User Interfaces (VR).IEEE,2020:828-835.
[29]CORSO A D,SALVI M,KOLB C,et al.Interactive Stable Ray Tracing[C]//Proceedings of High Performance Graphics(HPG'17).ACM,2017:1-10.
[30]BIGLER J,STEPHENS A,PARKER S G.Design for Parallel Interactive Ray Tracing Systems[C]//In Proceedings of the IEEE Symposium on Interactive Ray Tracing.IEEE,2006:187-195.
[31]YANG L,LIU S,SALVI M.A Survey of Temporal Antialiasing Techniques[J].Computer Graphics Forum,2020,39(2):607-621.
[32]SCHERZER D,LEI Y,MATTAUSCH O,et al.Temporal Coherence Methods in Real-Time Rendering[J].Computer Gra-phics Forum,2012,31(8):2378-2408.
[1] 苏庆华, 付景超, 谷焓, 张姗姗, 李奕飞, 江方舟, 白翰林, 赵地. 前列腺癌辅助诊断GPU并行算法设计[J]. 计算机科学, 2019, 46(11A): 524-527.
[2] 杨际祥. UCMLib:一种多核多线程编程库[J]. 计算机科学, 2016, 43(4): 188-191.
[3] 陈庆奎. 基于强化学习的多机群网格资源调度模型[J]. 计算机科学, 2007, 34(11): 67-70.
[4] . 集群环境下的并行聚类算法之研究[J]. 计算机科学, 2007, 34(10): 195-199.
[5] 佘春东 吴跃 孙世新 李磊 车著明. 一种高效的并行挖掘频繁序列的算法[J]. 计算机科学, 2004, 31(10): 203-205.
[6] 黄其军 丁阳 余华山 丁文魁 许卓群. 基于规范划分集的并行循环编译框架[J]. 计算机科学, 2002, 29(11): 1-8.
[7] 李晓明. 数据并行计算:概念,模型与系统[J]. 计算机科学, 2000, 27(6): 1-5.
[8] 韩天舒 胡铭曾. 数据并行语言中的扩展结构[J]. 计算机科学, 1998, 25(3): 38-40.
[9] 张可军 杨桃栏. 数据并行的计算模式,语言及其编译[J]. 计算机科学, 1993, 20(5): 54-62.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] 徐健锋,何宇凡,张远健,汤涛. 基于弯曲距离三支决策的时序相似性算法[J]. 计算机科学, 2017, 44(9): 40 -44 .
[2] 余未,郑吉平,王海翔,王永阁,陈嘉良,江顺青. 空间Skyline查询处理:应用、研究与挑战[J]. 计算机科学, 2017, 44(2): 1 -16 .
[3] 王冲鶄,赵旭,刘允才. 基于混合特征映射的密集场景运动模式分析[J]. 计算机科学, 2014, 41(10): 106 -109 .
[4] 陈风,田雨波,杨敏. 基于CUDA的并行粒子群优化算法研究及实现[J]. 计算机科学, 2014, 41(9): 263 -268 .
[5] 王轩,王振兴,王禹,张连成. SSI:一种IPv6/IPv4多址同源识别模型[J]. 计算机科学, 2014, 41(8): 139 -143 .
[6] 孙涛,叶新铭. 一种针对CP-nets并发模型的验证方法[J]. 计算机科学, 2014, 41(7): 135 -139 .
[7] 李新国,李鹏伟,傅建明,丁笑一. 一种安全风险可控的弹性移动云计算通用框架[J]. 计算机科学, 2015, 42(Z11): 357 -363 .
[8] 刘益含,闫德勤,刘彩凤. 医学图像配准分类研究[J]. 计算机科学, 2015, 42(11): 22 -27 .
[9] 余一骄,刘芹. 大规模中文语料库检索技术研究[J]. 计算机科学, 2015, 42(2): 217 -223 .
[10] 陈燕,吴赞红,王博,任海军,孔维禅. 智能配用电业务接入网络支持的关键技术研究[J]. 计算机科学, 2016, 43(Z6): 558 -560 .