The Community for Technology Leaders
RSS Icon
Issue No.06 - November/December (2010 vol.16)
pp: 1468-1476
Stefan Bruckner , Simon Fraser University, Canada
Torsten Möller , Simon Fraser University, Canada
Graphics artists commonly employ physically-based simulation for the generation of effects such as smoke, explosions, and similar phenomena. The task of finding the correct parameters for a desired result, however, is difficult and time-consuming as current tools provide little to no guidance. In this paper, we present a new approach for the visual exploration of such parameter spaces. Given a three-dimensional scene description, we utilize sampling and spatio-temporal clustering techniques to generate a concise overview of the achievable variations and their temporal evolution. Our visualization system then allows the user to explore the simulation space in a goal-oriented manner. Animation sequences with a set of desired characteristics can be composed using a novel search-by-example approach and interactive direct volume rendering is employed to provide instant visual feedback.
Visual exploration, visual effects, clustering, time-dependent volume data
Stefan Bruckner, Torsten Möller, "Result-Driven Exploration of Simulation Parameter Spaces for Visual Effects Design", IEEE Transactions on Visualization & Computer Graphics, vol.16, no. 6, pp. 1468-1476, November/December 2010, doi:10.1109/TVCG.2010.190
[1] W. Aigner, S. Miksch, W. Müller, H. Schumann, and C. Tominski, Visualizing time-oriented data - a systematic view. Computers & Graphics, 31 (3): 401–409, 2007.
[2] H. Akiba and K.-L. Ma, A tri-space visualization interface for analyzing time-varying multivariate volume data. In Proceedings of EuroVis 2007, pages 115–122, 2007.
[3] H. Akiba, K.-L. Ma, and N. Fout, Simultaneous classification of time-varying volume data based on the time histogram. In Proceedings of Euro Vis 2006, pages 1–8, 2006.
[4] H. Akiba, C. Wang, and K.-L. Ma, AniViz: A template-based animation tool for volume visualization. IEEE Computer Graphics and Applications (to appear), 2010.
[5] N. Andrienko, G. Andrienko, and P. Gatalsky, Exploratory spatiotemporal visualization: an analytical review. Journal of Visual Languages & Computing, 14 (6): 503–541, 2003.
[6] M. Ankerst, S. Berchtold, and D. Keim, Similarity clustering of dimensions for an enhanced visualization of multidimensional data. Proceedings of IEEE Info Vis 1998, pages 52–60, 1998.
[7] A. Bangor, P. Kortum, and J. Miller, An empirical evaluation of the system usability scale. International Journal of Human-Computer Interaction, 24 (6): 574–594, 2008.
[8] A. Bangor, P. Kortum, and J. Miller, Determining what individual SUS scores mean: Adding an adjective rating scale. Journal of Usability Studies, 4 (3): 114–123, 2009.
[9] D. Birant and A. Kut, ST-DBSCAN: An algorithm for clustering spatial-temporal data. Data & Knowledge Engineering, 60 (1): 208–221, 2007.
[10] G. Daniel and M. Chen, Video visualization. In Proceedings of IEEE Visualization 2003, pages 409–416, 2003.
[11] N. Elmqvist, J. Stasko, and P. Tsigas, Datameadow: A visual canvas for analysis of large-scale multivariate data. In Proceedings of VAST 2007, pages 187–194, 2007.
[12] M. Ester, H.-P. Kriegel, J. Sander, and X. Xu, A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of Knowledge Discovery and Data Mining 1996, pages 226–231, 1996.
[13] J. Fails, A. Karlson, L. Shahamat, and B. Shneiderman, A visual interface for multivariate temporal data: Finding patterns of events across multiple histories. In Proceedings of VAST 2006, pages 167–174, 2006.
[14] Z. Fang, T. Möller, G. Hamarneh, and A. Celler, Visualization and exploration of time-varying medical image data sets. In Proceedings of Graphics Interface 2007, pages 281–288, 2007.
[15] A. Hanjalic, Shot-boundary detection: unraveled and resolved? IEEE Transactions on Circuits and Systems for Video Technology, 12 (2): 90–105, 2002.
[16] A. Hanjalic and H. Zhang, An integrated scheme for automated video abstraction based on unsupervised cluster-validity analysis. IEEE Transactions on Circuits and Systems for Video Technology, 9 (8): 1280–1289, 1999.
[17] A. Hanson and P. Heng, Four-dimensional views of 3D scalar fields. In Proceedings of IEEE Visualization 1992, pages 84–91, 1992.
[18] M. Harrower and C. Brewer, An online tool for selecting colour schemes for maps. The Cartographic Journal, 40 (1): 27–37, 2003.
[19] S. Havre, E. Hetzler, P. Whitney, and L. Nowell, ThemeRiver: visualizing thematic changes in large document collections. IEEE Transactions on Visualization and Computer Graphics, 8 (1): 9–20, 2002.
[20] H. Hochheiser and B. Shneiderman, Dynamic query tools for time series data sets: Timebox widgets for interactive exploration. Information Visualization, 3 (1): 1–18, 2004.
[21] S. Johansson and J. Johansson, Interactive dimensionality reduction through user-defined combinations of quality metrics. IEEE Transactions on Visualization and Computer Graphics, pages 993–1000, 2009.
[22] T.-Y. Lee and H.-W. Shen, Visualization and exploration of temporal trend relationships in multivariate time-varying data. IEEE Transactions on Visualization and Computer Graphics, 15 (6): 1359–1366, 2009.
[23] K.-L. Ma, Image graphs - a novel approach to visual data exploration. Proceedings of IEEE Visualization 1999, pages 81–513, 1999.
[24] K.-L. Ma, Visualizing time-varying volume data. Computing in Science & Engineering, 5 (2): 34–42, 2003.
[25] J. Marks, B. Andalman, P. Beardsley, W. Freeman, S. Gibson, J. Hod-gins, T. Kang, B. Mirtich, H. Pfister, W. Ruml, K. Ryall, J. Seims, and S. Shieber, Design galleries: A general approach to setting parameters for computer graphics and animation. In Proceedings of ACM SIGGRAPH 1997, pages 389–400, 1997.
[26] M. Monks, B. Oh, and J. Dorsey, Audioptimization: Goal-based acoustic design. IEEE Computer Graphics and Applications, 20 (3): 76–91, 2000.
[27] P. Muigg, J. Kehrer, S. Oeltze, H. Piringer, H. Doleisch, B. Preim, and H. Hauser, A four-level focus+context approach to interactive visual analysis of temporal features in large scientific data. Computer Graphics Forum, 27 (3): 775–782, 2008.
[28] C.-W. Ngo, T.-C. Pong, and H.-J. Zhang, On clustering and retrieval of video shots. In Proceedings of Multimedia 2001, pages 51–60, 2001.
[29] S. Obayashi, Evolutionary Multi-Objective Optimization and Visualization, chapter 16, pages 175–185. Springer, 2005.
[30] D. Silver and X. Wang, Tracking and visualizing turbulent 3D features. IEEE Transactions on Visualization and Computer Graphics, 3 (2): 129–141, 1997.
[31] R. Smith, R. Pawlicki, I. Kókai, J. Finger, and T. Vetter, Navigating in a shape space of registered models. IEEE Transactions on Visualization and Computer Graphics, 13 (6): 1552–1559, 2007.
[32] A. Treuille, A. Lewis, and Z. Popović, Model reduction for real-time fluids. ACM Transactions on Graphics, 25 (3): 826–834, 2006.
[33] B. Truong and S. Venkatesh, Video abstraction: A systematic review and classification. ACM Transactions on Multimedia Computing, Communications, and Applications, 3 (1): 1–37, 2007.
[34] J. van Wijk and R. van Liere, Hyperslice: visualization of scalar functions of many variables. In Proceedings of IEEE Visualization 1993, pages 119–125, 1993.
[35] L. Wang, Y. Zhang, and J. Feng, On the Euclidean distance of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27 (8): 1334–1339, 2005.
[36] M. Wohlfart and H. Hauser, Story telling for presentation in volume visualization. In Proceedings of Euro Vis 2007, pages 91–98, 2007.
[37] J. Woodring and H.-W. Shen, Chronovolumes: a direct rendering technique for visualizing time-varying data. In Proceedings of Volume Graphics 2003, pages 27–34, 2003.
[38] J. Woodring and H.-W. Shen, Multi-variate, time varying, and comparative visualization with contextual cues. IEEE Transactions on Visualization and Computer Graphics, 12 (5): 909–916, 2006.
[39] J. Woodring and H.-W. Shen, Multiscale time activity data exploration via temporal clustering visualization spreadsheet. IEEE Transactions on Visualization and Computer Graphics, 15 (1): 123–137, 2009.
[40] J. Woodring, C. Wang, and H.-W. Shen, High dimensional direct rendering of time-varying volumetric data. In Proceedings of IEEE Visualization 2003, pages 417–424, 2003.
[41] E. Wu, Y. Liu, and X. Liu, An improved study of real-time fluid simulation on GPU. Computer Animation and Virtual Worlds, 15 (34): 139–146, 2004.
12 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool