A Parallel Research On Feature Detection And Feature Matching Based On3D Reconstruction With Multi-View

It is an important part of computer vision that based on multi-view reconstruction.The core of the algorithm is that we can generate a sparse three-dimensional point cloud data after the calculation with the multiple images as input, get a dense three-dimensional point cloud data and get the three-dimensional model by the surface reconstruction. It is widely used in protection of cultural relics, enhancement display, virtual reality,3D object recognition and digital cities. It is difficult to get the result of the algorithm which has a huge amount of computation through patch expansion and filter. In summary, we present an algorithm for reconstruction which based a heterogeneous architecture in this paper by analyze the feature detection and feature matching.we designed a parallel optimization algorithm which consider both the reconstruction accuracy and efficiency by taking advantage of CUDA and OpenMP. Finally, we design and develop an automated GUI-based end-to-end3D reconstruction system from images.First, we introduce the whole process of algorithm about3D reconstruction.In the part of feature detection, we focuse on the feature detection about SURF which compared to the Harris and DoG operator.In the part of feature matching,we introduce the NCC matching algorithm.Finally,through the RANSAC algorithm remove the mismatching points.It is the major part that designed and implemented the SURF and RANSAC algorithm through the OpenMP and CUDA.we given the optimization methods about the algorithm in OpenCV.Final,we give a software system based on multi-view3D reconstruction which can give a good result.