基于众源影像的三维重建方法

3D Reconstruction Method Based on Crowd-sourced Images

目前,众源影像存在获取数据难以筛选,进而导致从影像中生成的点云数据产生几何缺失和含有大量噪声等问题。为了解决这个问题,实现了一种基于众源影像的三维重建方法。首先,采用基于网站API(application programming interface)和基于网页解析的方法获取众源影像数据,然后借助深度学习对获取得到的众源影像进行筛选,获取高质量的众源影像数据,最后运用运动恢复结构(structure from motion,SFM)算法完成三维重建。论文利用众源影像获取场景的三维结构,对生成的点云模型进行对比和分析,得到了经深度学习算法筛选的图片集更适用于三维重建的结论,以解决众源影像这一新兴数据源在三维建模应用时出现的弊端和不足。

At present,It is difficult to filter the data from crowd-sourced images,which leads to the geometric missing and noises in the point cloud data generated from the images.To solve this problem,a method of 3D reconstruction based on crowd-sourced images was presented.Firstly,the methods based on website application programming interface(API)and web page analysis were used to obtain the crowd-sourced images.Then,the crowd-sourced images were filtered by deep learning to obtain high-quality crowd-sourced pictures data.Finally,the structure from motion(SFM)algorithm was used to complete the 3D reconstruction based on the filtered crowd-sourced images.It is concluded that image set screened by deep learning algorithm is more suitable for 3D reconstruction,so as to solve the disadvantages and deficiencies of crowd-sourced image,an emerging data source,in the application of 3D modeling.