Extraction Of Quasi Dense 3D Point Cloud Based On Close Range Image

With the continuous development and progress of society and the increasing market demand,3D reconstruction has played an important role in many aspects of society.The traditional 3D reconstruction scheme has been difficult to sustain in the face of increasingly stringent requirements.Because of the advantages of low threshold,data diversity and low cost,the image-based 3D reconstruction scheme is loved by most scholars.However,there are some problems in the existing 3D reconstruction,such as sparse number of matching points,uneven distribution,not considering the line feature area and insufficient accuracy.In this paper,a hierarchical point quasi dense matching algorithm based on triangular constraint and propagation is proposed.The algorithm constructs a triangular network from sparse matching points,and extracts the line features of the reference image,and uses the intersection of the triangular network and the line segment and the midpoint of the triangular network As matching primitives,the overlapping descriptors of sub regions are constructed,and the Euclidean distance is used as the judgment basis.According to the experimental results,this paper obtains reliable dense matching points,and combined with 3D reconstruction,can obtain good visual experience.The main contents of this paper are as follows:(1)Sub region overlap descriptor Construction: take the pixel index of the current point as the center,establish the description region,set the diagonal coordinates of the sub region by using the pixel index of the target point and the coordinates of the four corner points,there will be partial overlap between the adjacent sub regions,then divide the four sub regions into four block regions,and count the four block regions with Gaussian weighting Then the mean vector and standard deviation vector are normalized.Finally,the descriptor vectors of four sub regions are connected to get the overlapping descriptor vectors of sub regions,and the matching points are determined by combining Euclidean distance.(2)Multi constraint matching: if the initial matching fails,the matching under multi constraint conditions will be carried out.Multi constraint matching is to give corresponding weights to descriptor constraint,epipolar constraint,affine transformation and triangle constraint respectively,and construct score calculation formula.The one with the highest score and exceeding the set threshold is the success point of matching.(3)Experimental verification and analysis: several groups of different image quasi dense matching algorithm experiments and quasi dense matching algorithm experiments of different algorithms are carried out respectively,and the parameter selection is described in detail.Through the comparative analysis of the experimental results,the good applicability and stability of the quasi dense matching algorithm proposed in this paper are verified.At the same time,image sparse reconstruction and dense re matching combined with this algorithm are carried out The experimental results show the robustness and universality of the quasi dense matching algorithm.This paper has 56 pictures,6 tables and 112 references.