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A Multiresolution Terrain Model for Efficient Visualization Query Processing
October 2006 (vol. 18 no. 10)
pp. 1382-1396
Multiresolution Triangular Mesh (MTM) models are widely used to improve the performance of large terrain visualization by replacing the original model with a simplified one. MTM models, which consist of both original and simplified data, are commonly stored in spatial database systems due to their size. The relatively slow access speed of disks makes data retrieval the bottleneck of such terrain visualization systems. Existing spatial access methods proposed to address this problem rely on main-memory MTM models, which leads to significant overhead during query processing. In this paper, we approach the problem from a new perspective and propose a novel MTM called direct mesh that is designed specifically for secondary storage. It supports available indexing methods natively and requires no modification to MTM structure. Experiment results, which are based on two real-world data sets, show an average performance improvement of 5-10 times over the existing methods.

[1] W. Piekarski and B. Thomas, “ARQuake: The Outdoor Augmented Reality Gaming System,” Comm. ACM, vol. 45, no. 1, pp. 36-38, 2002.
[2] M.C. Whitton, “Making Virtual Environments Compelling,” Comm. ACM, vol. 46, no. 7, pp. 40-47, 2003.
[3] D. Green, J. Cosmas, T. Itagaki, M. Waelkens, R. Degeest, and E. Grabczewski, “A Real Time 3D Stratigraphic Visual Simulation System for Archaeological Analysis and Hypothesis Testing,” Proc. Conf. Virtual Reality, Archeology, and Cultural Heritage, pp. 271-278, 2001.
[4] S. Kiss and A. Nijholt, “Viewpoint Adaptation During Navigation Based on Stimuli from the Virtual Environment,” Proc. Eighth Int'l Conf. Third Web Technology, pp. 19-26, 2003.
[5] B. Benes and R. Forsbach, “Parallel Implementation of Terrain Erosion Applied to the Surface of Mars,” Proc. First Int'l Conf. Computer Graphics, Virtual Reality, and Visualisation, pp. 53-57, 2001.
[6] T. Gerstner, D. Meetschen, S. Crewell, M. Griebel, and C. Simmer, “A Case Study on Multiresolution Visualization of Local Rainfall from Weather Radar Measurements,” Proc. Conf. Visualization, pp. 533-536, 2002.
[7] J. Randall, W. Hill, Y. Kim, and J. Gratch, “Anticipating Where to Look: Predicting the Movements of Mobile Agents in Complex Terrain,” Proc. Int'l Joint Conf. Autonomous Agents and Multiagent Systems, pp. 821-827, 2002.
[8] B. Ben-Moshe, J.S.B. Mitchell, M.J. Katz, and Y. Nir, “Visibility Preserving Terrain Simplification: An Experimental Study,” Proc. 18th Ann. Symp. Computational Geometry, pp. 303-311, 2002.
[9] M. Garland, “Multiresolution Modeling: Survey & Future Opportunities,” Eurographics '99 State of the Art Reports, pp. 111-131, 1999.
[10] L. Shou, Z. Huang, and K.-L. Tan, “HDoV-Tree: The Structure, the Storage, the Speed,” Proc. 19th Int'l Conf. Data Eng. (ICDE), pp. 557-568,
[11] K. Xu, “Database Support for Multiresolution Terrain Visualization,” Proc. 14th Australian Database Conf. (ADC), pp. 153-160, 2003.
[12] C. Decoro and R. Pajarola, “XFastMesh: Fast View-Dependent Meshing from External Memory,” Proc. Conf. Visualization, pp. 363-370, 2002.
[13] P. Lindstrom, “Out-of-Core Construction and Visualization of Multiresolution Surfaces,” Proc. Symp. Interactive 3D Graphics, pp. 93-102, 2003.
[14] M. Isenburg and S. Gumhold, “Out-of-Core Compression for Gigantic Polygon Meshes,” ACM Trans. Graphics, vol. 22, no. 3, pp. 935-942, 2003.
[15] H. Hoppe, “Smooth View-Dependent Level-of-Detail Control and Its Application to Terrain Rendering,” Proc. IEEE Visualization, pp. 35-42, 1998.
[16] M. Kofler, M. Gervautz, and M. Gruber, “R-Trees for Organizing and Visualizing 3D GIS Database,” J. Visualization and Computer Animation, no. 11, pp. 129-143, 2000.
[17] L. Shou, C. Chionh, Y. Ruan, Z. Huang, and K.L. Tan, “Walking through a Very Large Virtual Environment in Real-Time,” Proc. 27th Int'l Conf. Very Large Data Bases, pp. 401-410, 2001.
[18] N. Beckmann, H. Kriegel, R. Schneider, and B. Seeger, “The R*-Tree: An Efficient and Robust Access Method for Points and Rectangles,” Proc. Ninth ACM SIGACT-SIGMOD-SIGART Symp. Principles of Database Systems, pp. 322-331, 1990.
[19] K. Xu, X. Zhou, and X. Lin, “Direct Mesh: A Multiresolution Approach to Terrain Visualization,” Proc. 20th Int'l Conf. Data Eng., pp. 766-777, 2004.
[20] F.P. Preparata and M.I. Shamos, Computational Geometry: An Introduction. Springer-Verlag, 1985.
[21] H. Hoppe, “Progressive Meshes,” Proc. 23rd Int'l Conf. Computer Graphics and Interactive Techniques (SIGGRAPH '96), pp. 99-108, 1996.
[22] H. Hoppe, “View-Dependent Refinement of Progressive Meshes,” Proc. 24th Int'l Conf. Computer Graphics and Interactive Techniques (SIGGRAPH '97), pp. 189-198, 1997.
[23] M. Kofler, “R-Trees for Visualizing and Organizing Large 3D GIS Databases,” PhD dissertation, Technische Universitat Graz, 1998.
[24] R.A. Finkel and J.L. Bentley, “Quad Trees: A Data Structure for Retrieval On Composite Keys,” Acta Informatica, vol. 4, pp. 1-9, 1974.
[25] S. Shekhar and D.-R. Liu, “CCAM: A Connectivity-Clustered Access Method for Aggregate Queries on Transportation Networks: A Summary of Results,” Proc. 11th Int'l Conf. Data Eng., pp. 410-419, 1995.
[26] D. Greene, “An Implementation and Performance Analysis of Spatial Data Access Methods,” Proc. Fifth Int'l Conf. Data Eng., pp. 606-615, 1989.
[27] J.T. Robinson, “The K-D-B-Tree: A Search Structure for Large Multidimensional Dynamic Indexes,” Proc. ACM Sigmod Int. Conf. Management of Data, pp. 10-18, 1981.
[28] K.-Y. Whang and R. Krishnamurthy, “The Multilevel Grid File-A Dynamic Hierarchical Multidimensional File Structure,” Proc. Second Int'l Symp. Database Systems for Advanced Applications, pp. 449-459, 1992.
[29] N. Beckmann, H. Kriegel, R. Schneider, and B. Seeger, “The R*-Tree: An Efficient and Robust Access Method for Points and Rectangles,” Proc. Ninth ACM-SIGMOD Symp. Principles of Database Systems, pp. 322-331, 1990.
[30] C. Faloutsos and I. Kamel, “Beyond Uniformity and Independence: Analysis of R-Trees Using the Concept of Fractal Dimension,” Proc. 13th ACM SIGACT-SIGMOD-SIGART Symp. Principles of Database Systems, pp. 4-13, 1994.
[31] J. Jin, N. An, and A. Sivasubramaniam, “Analyzing Range Queries On Spatial Data,” Proc. 16th Int'l Conf. Data Eng., pp. 525-534, 2000.
[32] I. Kamel and C. Faloutsos, “On Packing R-Trees,” Proc. Second ACM Int'l Conf. Information and Knowledge Management, pp. 490-499, 1993.
[33] B. Pagel, H. Six, H. Toben, and P. Widmayer, “Towards an Analysis of Range Query Performances,” Proc. ACM-SIGMOD Symp. Principles of Database Systems, pp. 214-221, 1993.
[34] G. Proietti and C. Faloutsos, “I/O Complexity for Range Queries on Region Data Stored Using an R-Tree,” Proc. 15th Int'l Conf. Data Eng., pp. 628-635, 1999.
[35] Y. Theodoridis and T. Sellis, “A Model for the Prediction of R-Tree Performance,” Proc. 15th ACM SIGACT-SIGMOD-SIGART Symp. Principles of Database Systems, pp. 161-171, 1996.

Index Terms:
Multiresolution visualization, spatial database systems.
Kai Xu, Xiaofang Zhou, Xuemin Lin, Heng Tao Shen, Ke Deng, "A Multiresolution Terrain Model for Efficient Visualization Query Processing," IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 10, pp. 1382-1396, Oct. 2006, doi:10.1109/TKDE.2006.151
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