A Study On Simplification Algorithm Of Large-scale Point Cloud In Intelligent Construction Scene

For the construction industry,the labor-intensive development is not appropriate for the development of the tide.The intelligent technology of building develops rapidly.Especially in recent years,three-dimensional(3D)scanning technology has been widely concerned by engineers and researchers.In the construction industry,3D laser is widely used in the quality detection of building components and reverse modeling.3D point cloud technology has a profound impact on the intelligent construction,but it is difficult to be widely used in the actual construction scanning data.One of the important reasons is that the scanning object in actual production is not a single building component,but also the whole building scene.The point cloud data(PCD)scanned from laser has a number of point cloud and is noisy,which brings great burden to the storage,processing and application of PCD.The actual data brings a greatly difficulties for the point cloud application in the actual work.Therefore,in order to conduct the practical application of building PCD,it is necessary to simplification 3D PCD.Based on the existing simplification algorithm,this paper studies the limitations and drawbacks of those algorithms under large volume data.We proposed a method based on the strategy of divide and conquer to achieve an invariance solution of algorithm.Based on the existing big data technique,a PCD simplification system is built,and the relevant experimental demonstration is carried out.The main work of this paper is as follows(1)In order to solve the problem of large volume point cloud simplification,a PCD simplification method based on the concept of natural neighborhood is proposed.At first,Based on the feature extraction process by graph signal and the concept of natural neighborhood point set,a decomposed feature extraction algorithm is developed to handle each PCD subset sliced.Secondly,decomposed graph filters are successfully designed for the large volume PCD simplification problem based on original algorithm.(2)Theoretical results guarantee the invariance of simplification in the proposed decomposed framework.(3)Based on the decomposed simplification method and the basic principle of MapReduce,a distributed PCD simplification framework is realized.(4)In order to improve robustness of algorithm in application,a distributed optimization method of PCD simplification,which aims to strike a balance between preserving sharp features and keeping uniform density,is proposed based on the principle of ADMM consensus.Based on this,a distributed PCD simplification algorithm based on MPI is implemented.