The Research Of Surface Reconstruction Algorithm For Large-scale Scene Point Cloud

The Surface reconstruction of large-scale scene point cloud is mainly focused on laser scanning point cloud data or point cloud which was reconstructed from sequence image,it has an important role in heritage conservation,urban modeling,virtual vision and other fields.This thesis mainly research Surface reconstruction algorithm for large-scale scene point cloud data sequence reconstructed from image,and Poisson surface reconstruction algorithm is applied to the scene point cloud surface reconstruction.This thesis present a Poisson surface reconstruction improved algorithm that introduces interpolation constraint.This paper also proposed Poisson surface reconstruction based GPU parallelization for speedability while ensure accuracy.Firstly,large-scale 3D reconstruction from image sequences scene.Through image sequences SIFT feature extraction and matching,using multi-view geometry theory recovery camera position and attitude of the three-dimensional structure of scene.Then use the bundle adjustment algorithm set constraints for improving the accuracy of the reconstruction,because reconstruction is based on image feature extraction,so the point cloud data are very sparse and can not recover the fine structure of the scene,so this thesis reconstructs dense point cloud using multi-view matching algorithm(PMVS),then obtain fine scene structure for surface reconstruction.Secondly,this thesis focuses on two classical surface reconstruction algorithm for large-scale scene point cloud surface reconstruction.This thesis is mainly focused on The PowerCrust surface reconstruction algorithm and Poisson surface reconstruction algorithm.The Power Crust surface reconstruction algorithm use a central axis conversion to reconstruct surface based on Delaunay Triangulation.This reconstruction algorithm obtain a smooth surface model,but Delaunay Triangulation processes are complex and take a lot of time,So this thesis puts forward the improved algorithm based on downsampling.Poisson surface reconstruction algorithm is a implicit function algorithm which can obtain a smooth surface,but it has the smoothing over problems,so this thesis propose a interpolation constraint reconstruction algorithms,The extension can be interpreted as a generalization of the underlying mathematical framework to a screened Poisson equation.The improved algorithm can obtain higher-quality surface.Then this thesis presents a parallel surface reconstruction algorithm that runs entirely on the GPU.Like existing implicit surface reconstruction methods,the algorithm first builds an octree for the given set of oriented points,then computes an implicit function over the space of the octree,and finally extracts an isosurface as a water-tight triangle mesh.A key component of our algorithm is a novel technique for octree construction on the GPU.This technique builds octrees in real-time and uses level-order traversals to exploit the fine-grained parallelism of the GPU.Moreover,the technique produces octrees that provide fast access to the neighborhood information of each octree node,which is critical for fast GPU surface reconstruction.GPU algorithm performs Poisson surface reconstruction,which produces high quality surfaces through a global optimization.Finally,the proposed Poisson surface reconstruction algorithm can get fine structure the scene,at the same time,the parallel reconstruction algorithm improves the speed of reconstruction for large-scale scene point cloud.The final experiment proved that the accuracy of the algorithm didn't change and the parallel algorithm greatly improved the algorithm's speed.