论文标题
有效的视图聚类和城市尺度3D重建的选择
Efficient View Clustering and Selection for City-Scale 3D Reconstruction
论文作者
论文摘要
图像数据集的大小一直在稳步增长，损害了大规模3D重建方法的可行性和效率。在本文中，提出了一种新颖的方法，用于扩展多视图立体声（MVS）算法，直到任意大量图像集合。具体而言，重建整个城市的3D模型的问题是针对目标的，从一组装有几个高分辨率摄像头的移动车辆收购的视频开始。最初，提出的方法利用了姿势和几何形状的大约均匀分布，并构建了一组重叠的簇。然后，为每个群集制定了整数线性编程（ILP）问题，以选择一个最佳的视图子集，以保证可见性和可匹配性。最后，分别计算并合并了每个集群的本地点云。由于聚类独立于成对可见性信息，因此所提出的算法运行速度比现有文献更快，并且可以进行大规模的并行化。讨论了对城市数据的广泛测试，以显示这种方法的有效性和可扩展性。
Image datasets have been steadily growing in size, harming the feasibility and efficiency of large-scale 3D reconstruction methods. In this paper, a novel approach for scaling Multi-View Stereo (MVS) algorithms up to arbitrarily large collections of images is proposed. Specifically, the problem of reconstructing the 3D model of an entire city is targeted, starting from a set of videos acquired by a moving vehicle equipped with several high-resolution cameras. Initially, the presented method exploits an approximately uniform distribution of poses and geometry and builds a set of overlapping clusters. Then, an Integer Linear Programming (ILP) problem is formulated for each cluster to select an optimal subset of views that guarantees both visibility and matchability. Finally, local point clouds for each cluster are separately computed and merged. Since clustering is independent from pairwise visibility information, the proposed algorithm runs faster than existing literature and allows for a massive parallelization. Extensive testing on urban data are discussed to show the effectiveness and the scalability of this approach.
