Multiview Structure from Motion is a fundamental and challenging computer
vision problem. A recent deep-based approach was proposed utilizing matrix
equivariant architectures for the simultaneous recovery of camera pose and 3D
scene structure from large image collections. This work however made the
unrealistic assumption that the point tracks given as input are clean of
outliers. Here we propose an architecture suited to dealing with outliers by
adding an inlier/outlier classifying module that respects the model
equivariance and by adding a robust bundle adjustment step. Experiments
demonstrate that our method can be successfully applied in realistic settings
that include large image collections and point tracks extracted with common
heuristics and include many outliers.

多角度视图下的运动结构是一个基础且具有挑战性的计算机视觉问题，本文提出了一种基于深度学习的方法，利用矩阵等变体结构实现了从大量图像集合中同时恢复相机姿态和三维场景结构。通过添加一个适应于处理异常值的内禀 / 外禀分类模块并添加一个鲁棒的捆绑调整步骤，我们的方法可以成功地应用于包含常见启发式方法提取的具有许多异常值的大型图像集合的现实场景。