Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (1): 156-165.doi: 10.11896/jsjkx.211100183

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Cloth Simulation Filtering Algorithm with Topography Cognition

MENG Huaru, WU Guowei   

  1. School of Software Technology,Dalian University of Technology,Dalian,Liaoning 116621,China
  • Received:2021-11-17 Revised:2022-06-11 Online:2023-01-15 Published:2023-01-09
  • About author:MENG Huaru,born in 1996,postgra-duate.His main research interests include point cloud filtering algorithm design and point cloud based geological hazard identification.
    WU Guowei,born in 1973,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include advanced computing and intelligent system.

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Abstract: Digital elevation model(DEM) can reflect the topographic characteristics of an area and has a wide range of scientific research applications.Filtering LIDAR point cloud data,extracting the ground points and interpolating are common steps in constructing DEM.The filtering algorithm used in the process of point cloud filtering directly affects the accuracy of the final DEM.As a point cloud filtering algorithm,cloth simulation filtering(CSF) algorithm has the advantages of simple model and high filtering efficiency.It has high filtering accuracy for flat areas.However,when dealing with complex terrain areas,the accuracy of filtering results will be poor due to the internal elasticity and gravity inertia of the cloth model.In view of this,in order to improve the filtering accuracy and terrain adaptability of CSF algorithm in dealing with complex terrain areas,so as to improve the accuracy of constructing DEM,the cloth simulation filtering algorithm with terrain cognition(CSFTC) is proposed.The algorithm proposes a terrain-cognitive model.Based on the local distribution characteristics of point cloud data points,the terrain-cognitive model is constructed and extended to rough digital elevation model(R-DEM),which realizes the separation of macro terrain trend and micro terrain details through point cloud terrain normalization.Finally,the original CSF algorithm combined with R-DEM is used to realize point cloud filtering.Comparison experiment between CSFTC algorithm and the original CSF algorithm is designed.The average total error rate decreases from 9.30% to 5.10%,and the average type-II error rate decreases from 30.02% to 8.46%.Experimental results show that compared with the original CSF algorithm,the accuracy of CSFTC algorithm increases slightly in flat region and increases significantly in complex region,which improves the terrain adaptability of the algorithm.The significant decrease of type-II error is helpful to improve the accuracy of constructed DEM.

Key words: Terrain-cognitive model, Point cloud, Cloth simulation filtering, Digital elevation model, Terrain normalization

CLC Number: 

  • TP391
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