Organization and Management of Massive Terrain Data Using Block Quadtree

Guest Editor Huier Liu (North China University of Water Resources and Electric Power, China)

To alleviate the impact on terrain rendering caused by real-time scheduling deformation data, we presented an organization approach of massive terrain data based on block quadtree and Peano encoding non-pointer data index. First, we constructed block quadtree and reorganize blocks according to Peano filling curve, which could ensure location and continuity of block data and improve the speed of terrain rendering. Secondly, non-pointer data index based on block quadtree and Peano encoding was designed to realize the exact orientation of terrain data block. In addition, we adopt GPU minimum box visible detection and terrain data prediction strategy to realize dynamical management and real-time scheduling of terrain data. The experiment results show that the algorithm can improve the efficiency of data scheduling and achieve good performance for massive terrain rendering.

Journal: International Journal of Simulation: Systems, Science & Technology, IJSSST V17

Published: Jan 21, 2016

DOI: 10.5013/IJSSST.a.17.03.18