Please wait a minute...
浙江大学学报(理学版)  2023, Vol. 50 Issue (6): 681-691    DOI: 10.3785/j.issn.1008-9497.2023.06.003
第26届全国计算机辅助设计与图形学学术会议专题     
基于最优质量传输的Focus+Context可视化
苏科华1(),刘百略1,雷娜2(),李可涵1,顾险峰3
1.武汉大学 计算机学院, 湖北 武汉 430072
2.大连理工大学 国际信息与软件学院, 辽宁 大连 116024
3.纽约州立大学石溪分校 计算机系, 美国 石溪 11790
Focus+Context visualization based on optimal mass transportation
Kehua SU1(),Bailüe LIU1,Na LEI2(),Kehan LI1,Xianfeng GU3
1.School of Computer Science,Wuhan University,Wuhan 430072,China
2.International School of Information Science & Engineering,Dalian University of Technology,Dalian 116024,Liaoning Province,China
3.Department of Computer Science,State University of New York at Stony Brook,Stony Brook 11790,New York,USA
 全文: PDF(3910 KB)   HTML( 3 )
摘要:

在分辨率有限的显示设备上,Focus+Context技术可用于大型复杂模型的可视化。提出了一种基于最优质量传输的Focus+Context可视化方法。通过最优质量传输映射,对自身进行体积变形,将源测度(体素)转换为传输成本最小的目标测度;将求解最优质量传输问题等价于凸优化过程,转换为计算几何中经典的幂Voronoi图计算。与现有方法相比,本文方法具有坚实的理论基础,保证了解的存在性、唯一性和平滑性;允许用户精确控制目标测度,选择多个形状不规则的聚焦区域,使产生的变形是全局平滑的,并可自由翻转。用于自医学应用和科学仿真的几项体数据集,证明了所提方法是有效和高效的。

关键词: 辐射度全局光照常量时间    
Abstract:

In visualization field, Focus+Context techniques have been developed to visualize large, complex models on the display device with limited resolution. In this work, we propose a novel method for Focus+Context visualization based on optimal mass transportation. An optimal mass transportation map deforms a volume to itself, transforms the source measure (volumetric element) to the target measure with the minimal transportation cost. Solving the optimal mass transportation problem is equivalent to a convex optimization, and can be converted to computing power Voronoi diagrams in classical computational geometry. Comparing to existing approaches, the proposed method has solid theoretic foundation, which guarantees the existence, uniqueness and the smoothness of the solution. It allows the user to accurately control the target measure, and select multiple focus regions with irregular shapes. The resulting deformation is globally smooth and flipping free. Experiments with several volume data sets from medical applications and scientific simulations demonstrate the effectiveness and efficiency of our method.

Key words: radiosity    global illumination    constant time
收稿日期: 2023-06-12 出版日期: 2023-11-30
CLC:  TP 391.41  
基金资助: 国家自然科学基金资助项目(62272354)
通讯作者: 雷娜     E-mail: skh@whu.edu.cn;nalei@dlut.edu.cn
作者简介: 苏科华(1979—),ORCID:https://orcid.org/0000-0002-1384-9762,男,博士,教授,主要从事计算机图形学研究,E-mail:skh@whu.edu.cn.
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  
苏科华
刘百略
雷娜
李可涵
顾险峰

引用本文:

苏科华,刘百略,雷娜,李可涵,顾险峰. 基于最优质量传输的Focus+Context可视化[J]. 浙江大学学报(理学版), 2023, 50(6): 681-691.

Kehua SU,Bailüe LIU,Na LEI,Kehan LI,Xianfeng GU. Focus+Context visualization based on optimal mass transportation. Journal of Zhejiang University (Science Edition), 2023, 50(6): 681-691.

链接本文:

https://www.zjujournals.com/sci/CN/10.3785/j.issn.1008-9497.2023.06.003        https://www.zjujournals.com/sci/CN/Y2023/V50/I6/681

图1  上包络ℰh、凸包𝒞h、Voronoi单元分解𝒱(h)、幂Delaunay三角剖分𝒯h及其关系的二维类比
图2  用OMT技术放大的动脉瘤
图3  对盆栽模型测度的精确控制
图4  目标测度μ的平滑程度对脚模型变形平滑程度的影响
图5  NCAT phantom模型的F+C可视化结果
图6  多焦点多视角放大的CT膝关节模型的F+C可视化结果
模型数据来源运行时间/s分辨率
动脉瘤Philips Research,Hamburg,Germany118512×512×512
盆栽Rosttger S,VIS,University of Stuttgart156512×512×512
Philips Research,Hamburg,Germany182256×256×256
NCAT phantomSegars WP,Tsui BMW156512×512×512
CT膝关节Department of Radiology University of Iowa134440×440×440
表1  运行时间
1 WANG Y S, WANG C, LEE T Y, et al. Feature-preserving volume data reduction and Focus+ Context visualization[J]. IEEE Transactions on Visualization and Computer Graphics, 2010, 17(2): 171-181. DOI:10.1109/tvcg.2010.34
doi: 10.1109/tvcg.2010.34
2 GU X, LUO F, SUN J, et al. Variational principles for Minkowski type problems, discrete optimal transport, and discrete Monge-Ampere equations[J]. Asian Journal of Mathematics, 2016, 20(2): 383-398. doi:10.4310/ajm.2016.v20.n2.a7
doi: 10.4310/ajm.2016.v20.n2.a7
3 SARKAR M, BROWN M H. Graphical fisheye views of graphs[C]// SIGCHI Conference on Human Factors in Computing Systems. Monterey: SIGCHI, 1992: 83-91. DOI:10.1145/142750.142763
doi: 10.1145/142750.142763
4 KUMAR V R, EISING C, WITT C, et al. Surround-view fisheye camera perception for automated driving: Overview, survey & challenges[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(4): 3638-3659. DOI:10.1109/tits.2023. 3235057
doi: 10.1109/tits.2023. 3235057
5 BIER E A, STONE M C, PIER K, et al. Toolglass and magic lenses: The see-through interface[C]// 20th Annual Conference on Computer Graphics and Interactive Techniques. Anaheim: ACM, 1993: 73-80. DOI:10.1145/166117.166126
doi: 10.1145/166117.166126
6 AGLEDAHL S, STEED A. Magnification vision: A novel gaze-directed user interface[C]// 2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW). IEEE: Portugal, 2021: 474-475. DOI:10.1109/vrw52623.2021.00119
doi: 10.1109/vrw52623.2021.00119
7 CARPENDALE M S T, COWPERTHWAITE D J, FRACCHIA F D. Distortion viewing techniques for 3-dimensional data[C]// Proceedings of IEEE Symposium on Information Visualization. San Francisco: IEEE, 1996: 46-53. DOI:10.1109/infvis. 1996.559215 .
doi: 10.1109/infvis. 1996.559215
8 CARPENDALE M S T, COWPERTHWAITE D J, FRACCHIA F D. Multi-scale viewing[C]// 23th International Conference on Computer Graphics and Interactive Techniques. New Orleans: ACM, 1996: 149. DOI:10.1145/253607.253882
doi: 10.1145/253607.253882
9 CARPENDALE M S T, MONTAGNESE C. A framework for unifying presentation space[C]// 14th Annual ACM Symposium on User Interface Software and Technology. Orlando: ACM, 2001: 61-70. DOI:10.1145/502348.502358
doi: 10.1145/502348.502358
10 VIOLA I, KANITSAR A, GROLLER M E. Importance-driven volume rendering[C]// IEEE Visualization. Austin: IEEE, 2004: 139-145. DOI:10.1109/visual.2004.48
doi: 10.1109/visual.2004.48
11 VIOLA I, FEIXAS M, SBERT M, et al. Importance-driven focus of attention[J]. IEEE Transactions on Visualization and Computer Graphics, 2006, 12(5): 933-940. DOI:10.1109/tvcg.2006.152
doi: 10.1109/tvcg.2006.152
12 PIETRIGA E, APPERT C. Sigma lenses: Focus-Context transitions combining space, time and translucence[C]// SIGCHI Conference on Human Factors in Computing Systems. Florence: SIGCHI, 2008: 1343-1352. DOI:10.1145/1357054.1357264
doi: 10.1145/1357054.1357264
13 PIETRIGA E, BAU O, APPERT C. Representation-independent in-place magnification with sigma lenses[J]. IEEE Transactions on Visualization and Computer Graphics, 2009, 16(3): 455-467. doi:10.1109/tvcg.2009.98
doi: 10.1109/tvcg.2009.98
14 MCGUFFIN M J, TANCAU L, BALAKRISHNAN R. Using deformations for browsing volumetric data[C]// IEEE Visualization. Seattle: IEEE, 2003: 401-408. DOI:10.1109/visual.2003.1250400
doi: 10.1109/visual.2003.1250400
15 CORREA C, SILVER D, CHEN M. Illustrative deformation for data exploration[J]. IEEE Transactions on Visualization and Computer Graphics, 2007, 13(6): 1320-1327. DOI:10.1109/tvcg.2007.70565
doi: 10.1109/tvcg.2007.70565
16 WANG L, ZHAO Y, MUELLER K, et al. The magic volume lens: An interactive Focus+Context technique for volume rendering[C]// IEEE Visualization. Minneapolis: IEEE, 2005: 367-374. DOI:10.1109/visual.2005.1532818
doi: 10.1109/visual.2005.1532818
17 WANG Y S, LEE T Y, TAI C L. Focus+Context visualization with distortion minimization[J]. IEEE Transactions on Visualization and Computer Graphics, 2008, 14(6): 1731-1738. DOI:10.1109/tvcg.2008.132
doi: 10.1109/tvcg.2008.132
18 ZHAO X, ZENG W, GU X D, et al. Conformal magnifier: A Focus+ Context technique with local shape preservation[J]. IEEE Transactions on Visualization and Computer Graphics, 2012, 18(11): 1928-1941. doi:10.1109/tvcg.2012.70
doi: 10.1109/tvcg.2012.70
19 TAO J, WANG C, SHENE C K, et al. A deformation framework for Focus+Context flow visualization[J]. IEEE Transactions on Visualization and Computer Graphics, 2013, 20(1): 42-55. DOI:10.1109/tvcg.2013.100
doi: 10.1109/tvcg.2013.100
20 ZHAO X, SU Z, GU X D, et al. Area-preservation mapping using optimal mass transport[J]. IEEE Transactions on Visualization and Computer Graphics, 2013, 19(12): 2838-2847. DOI:10.1109/tvcg.2013.135
doi: 10.1109/tvcg.2013.135
21 BONNOTTE N. From Knothe's rearrangement to Brenier's optimal transport map[J]. SIAM Journal on Mathematical Analysis, 2013, 45(1): 64-87. DOI:10.1137/120874850
doi: 10.1137/120874850
22 KANTOROVICH L V. On a problem of Monge[J]. Journal of Mathematical Sciences, 2006, 133(4): 1383-1383. DOI:10.1007/s10958-006-0050-9
doi: 10.1007/s10958-006-0050-9
23 BRENIER Y. Polar factorization and monotone rearrangement of vector-valued functions[J]. Communications on Pure and Applied Mathematics, 1991, 44(4): 375-417. DOI:10.1002/cpa. 3160440402
doi: 10.1002/cpa. 3160440402
24 ALEXANDROV A D. Convex Polyhedra[M]. Berlin: Springer, 2005. doi:10.1007/b137434
doi: 10.1007/b137434
25 AURENHAMMER F. Power diagrams: Properties, algorithms and applications[J]. SIAM Journal on Computing, 1987, 16(1): 78-96. DOI:10.1137/0216006
doi: 10.1137/0216006
26 DE BERG M, CHEONG O, VAN KREVELD M J, et al. Computational Geometry (Algorithms and Applications)[M]. Berlin: Springer, 2008. doi:10.1007/978-3-540-77974-2
doi: 10.1007/978-3-540-77974-2
27 SI H. TetGen, a Delaunay-based quality tetrahedral mesh generator[J]. ACM Transactions on Mathematical Software, 2015, 41(2): 1-36. DOI:10. 1145/2629697
doi: 10. 1145/2629697
28 REUTER M, WOLTER F E, PEINECKE N. Laplace-Beltrami spectra as "Shape-DNA" of surfaces and solids[J]. Computer-Aided Design, 2006, 38(4): 342-366. DOI:10.1016/j.cad.2005. 10.011
doi: 10.1016/j.cad.2005. 10.011
[1] 刘圣军,滕子,王海波,刘新儒. 基于函数映射的二维形状内蕴对称检测算法[J]. 浙江大学学报(理学版), 2023, 50(6): 668-680.
[2] 刘泽润,尹宇飞,薛文灏,郭蕊,程乐超. 基于扩散模型的条件引导图像生成综述[J]. 浙江大学学报(理学版), 2023, 50(6): 651-667.
[3] 方于华,叶枫. MFDC-Net:一种融合多尺度特征和注意力机制的乳腺癌病理图像分类算法[J]. 浙江大学学报(理学版), 2023, 50(4): 455-464.
[4] 虞瑞麒,刘玉华,沈禧龙,翟如钰,张翔,周志光. 表征学习驱动的多重网络图采样[J]. 浙江大学学报(理学版), 2022, 49(3): 271-279.
[5] 钟颖,王松,吴浩,程泽鹏,李学俊. 基于SEMMA的网络安全事件可视探索[J]. 浙江大学学报(理学版), 2022, 49(2): 131-140.
[6] 祝锦泰, 叶继华, 郭凤, 江蕗, 江爱文. FSAGN: 一种自主选择关键帧的表情识别方法[J]. 浙江大学学报(理学版), 2022, 49(2): 141-150.
[7] 朱强,王超毅,张吉庆,尹宝才,魏小鹏,杨鑫. 基于事件相机的无人机目标跟踪算法[J]. 浙江大学学报(理学版), 2022, 49(1): 10-18.
[8] 杨猛,丁曙,马云涛,谢佳翊,段瑞枫. 基于纹理特征的小麦锈病动态模拟方法[J]. 浙江大学学报(理学版), 2022, 49(1): 1-9.
[9] 傅汝佳, 冼楚华, 李桂清, 万隽杰, 曹铖, 杨存义, 高月芳. 面向表型精确鉴定的豆株快速三维重建[J]. 浙江大学学报(理学版), 2021, 48(5): 531-539.
[10] 余鹏, 刘兰, 蔡韵, 何煜, 张松海. 基于单目摄像头的自主健身监测系统[J]. 浙江大学学报(理学版), 2021, 48(5): 521-530.
[11] 桂志强, 姚裕友, 张高峰, 徐本柱, 郑利平. 3D-power图的快速生成方法[J]. 浙江大学学报(理学版), 2021, 48(4): 410-417.
[12] 徐敏, 王科, 戴浩然, 罗晓博, 余炜伦, 陶煜波, 林海. 基于电子病历的乳腺癌群组与治疗方案可视分析[J]. 浙江大学学报(理学版), 2021, 48(4): 391-401.
[13] 邹北骥, 杨文君, 刘姝, 姜灵子. 面向自然场景图像的三阶段文字识别框架[J]. 浙江大学学报(理学版), 2021, 48(1): 1-8.
[14] 陈园琼, 邹北骥, 张美华, 廖望旻, 黄嘉儿, 朱承璋. 医学影像处理的深度学习可解释性研究进展[J]. 浙江大学学报(理学版), 2021, 48(1): 18-29.
[15] 邓惠俊. 排序支持的交互数据分类算法及其应用[J]. 浙江大学学报(理学版), 2021, 48(1): 9-17.