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View-graph Construction Framework for Robust and Efficient Structure-from-Motion
Pattern Recognition ( IF 8 ) Pub Date : 2020-10-01 , DOI: 10.1016/j.patcog.2020.107712
Hainan Cui , Tianxin Shi , Jun Zhang , Pengfei Xu , Yiping Meng , Shuhan Shen

Abstract A view-graph is vital for both the accuracy and robustness of structure-from-motion (SfM). Conventional matrix decomposition techniques treat all edges of view-graph equally; hence, many edge outliers are produced in matching pairs with fewer feature matches. To address this problem, we propose an incremental framework for view-graph construction, where the robustness of matched pairs that have a larger number of feature matches is propagated to their connected images. Given pairwise feature matches, a verified maximum spanning tree (VMST) is first constructed; for each edge in the VMST, we perform a local reconstruction and register its visible cameras. Based on the local reconstruction, pairwise relative geometries are computed and some new epipolar edges are produced. In this way, these newly computed edges inherit the robustness and accuracy of VMST, and by embedding them into VMST, our view-graph is constructed. We feed our view-graph into a standard SfM pipeline and compare this newly formed system with many of state-of-the-art SfM methods. The experimental results demonstrate that our view-graph provides a better foundation for conventional SfM systems, and enables them to reconstruct both general and ambiguous images.

中文翻译:

用于健壮和高效的运动结构的视图图构建框架

摘要 视图图对于运动结构(SfM)的准确性和鲁棒性都至关重要。传统的矩阵分解技术平等地对待视图图的所有边;因此,许多边缘异常值是在具有较少特征匹配的匹配对中产生的。为了解决这个问题,我们提出了一个用于视图图构建的增量框架,其中具有大量特征匹配的匹配对的鲁棒性被传播到它们的连接图像。给定成对特征匹配,首先构造经过验证的最大生成树(VMST);对于 VMST 中的每条边,我们执行局部重建并注册其可见相机。基于局部重建,计算成对的相对几何形状并产生一些新的对极边缘。这样,这些新计算的边继承了 VMST 的鲁棒性和准确性,通过将它们嵌入到 VMST 中,我们的视图图就被构建了。我们将视图图输入标准 SfM 管道,并将这个新形成的系统与许多最先进的 SfM 方法进行比较。实验结果表明,我们的视图图为传统的 SfM 系统提供了更好的基础,并使它们能够重建一般和模糊的图像。
更新日期:2020-10-01
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