A Parallel Algorithm of Model Simplification Under the Control of Parameter Based on Independent Set of Points

With the rapid development of data acquisition technology,the precision of point cloud of target object becoming more and more sophisticated.It's possible to build a high accuracy virtual simulation of the real world with the high-density data,but for its modelling and analyzing,the time-consuming is also growing.In this study,the spatial mesh model built through point cloud is treated as the studied object,and a parallel algorithm of model simplification under the control of parameter is studied.The steps are as follows:first,the K-adjacency table is established according to the topology of spatial mesh points;second,the independent set of points is calculated;third,the header element in each item of K-adjacency is taken as the center point,the projection plane is calculated with its neighbour points,then the K value is calculated;forth,the original model is transformed into the K value space,and the feature distribution of the model is studied;fifth,the model is simplified with the K value compared with the input parameter,loops until meet the condition;finally,the experiment is carried on series of spatial meshes.It's proved that the algorithm not only can simplify the model effectively,but also the degree of distortion is also under the control.