We propose a new hierarchical structure from motion system for close loop sequences. Our system includes a novel approach for clustering cameras into multiple sets, whose camera poses are initially reconstructed separately and later globally registered w.r.t a single coordinate frame. Each of the multiple sets is robustly reconstructed by a novel guarded least median square protocol. Our method is accelerated by reducing the parameter space of bundle adjustment in the local reconstruction optimizations. We also propose a new synthetic dataset that could be useful in 3D object reconstruction problems. Extensive experiments with both synthetic and real data were carried out to validate our method. Our system presented better results than ACTS and GPE in terms of rotation and translation errors in the camera pose estimations, and the accuracy is quite close to COLMAP while our method is much faster.

我们为闭环序列提出了一种来自运动系统的新分层结构。我们的系统包括一种新颖的方法，可将摄像机聚类为多个集合，其摄像机姿态最初是分别重建的，然后在单个坐标系中进行全局注册。多个集合中的每一个都通过新颖的保护最小中位数平方协议进行了稳健的重构。通过减少局部重建优化中包调整的参数空间来加快我们的方法。我们还提出了一个新的合成数据集，该数据集可用于3D对象重建问题。进行了综合和真实数据的广泛实验，以验证我们的方法。就相机姿态估计中的旋转和平移误差而言，我们的系统提供了比ACTS和GPE更好的结果，