Bao Yongtang, Lin Pengfei, Li Yao, Qi Yue, Wang Zhihui, Du Wenxiang, Fan Qing
College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China.
State Key Laboratory of Virtual Reality and Technology, Beihang University, Beijing 100191, China.
Sensors (Basel). 2021 Jun 7;21(11):3939. doi: 10.3390/s21113939.
Scene reconstruction uses images or videos as input to reconstruct a 3D model of a real scene and has important applications in smart cities, surveying and mapping, military, and other fields. Structure from motion (SFM) is a key step in scene reconstruction, which recovers sparse point clouds from image sequences. However, large-scale scenes cannot be reconstructed using a single compute node. Image matching and geometric filtering take up a lot of time in the traditional SFM problem. In this paper, we propose a novel divide-and-conquer framework to solve the distributed SFM problem. First, we use the global navigation satellite system (GNSS) information from images to calculate the GNSS neighborhood. The number of images matched is greatly reduced by matching each image to only valid GNSS neighbors. This way, a robust matching relationship can be obtained. Second, the calculated matching relationship is used as the initial camera graph, which is divided into multiple subgraphs by the clustering algorithm. The local SFM is executed on several computing nodes to register the local cameras. Finally, all of the local camera poses are integrated and optimized to complete the global camera registration. Experiments show that our system can accurately and efficiently solve the structure from motion problem in large-scale scenes.
场景重建使用图像或视频作为输入来重建真实场景的三维模型，在智慧城市、测绘、军事等领域有着重要应用。运动结构（SFM）是场景重建中的关键步骤，它从图像序列中恢复稀疏点云。然而，单个计算节点无法重建大规模场景。在传统的SFM问题中，图像匹配和几何滤波耗时很长。在本文中，我们提出了一种新颖的分治框架来解决分布式SFM问题。首先，我们利用图像中的全球导航卫星系统（GNSS）信息来计算GNSS邻域。通过将每个图像仅与有效的GNSS邻居进行匹配，大大减少了匹配的图像数量。这样，可以获得稳健的匹配关系。其次，将计算得到的匹配关系用作初始相机图，通过聚类算法将其划分为多个子图。在多个计算节点上执行局部SFM以对局部相机进行配准。最后，对所有局部相机姿态进行整合和优化，以完成全局相机配准。实验表明，我们的系统能够准确、高效地解决大规模场景中的运动结构问题。
