In this paper, we present a robust and efficient Structure from Motion pipeline for accurate 3D reconstruction under challenging environments by leveraging the camera pose information from a visual-inertial odometry. Specifically, we propose a geometric verification method to filter out mismatches by considering the prior geometric configuration of candidate image pairs. Furthermore, we introduce an efficient and scalable reconstruction approach that relies on batched image registration and robust bundle adjustment, both leveraging the reliable local odometry estimation. Extensive experimental results show that our pipeline performs better than the state-of-the-art SfM approaches in terms of reconstruction accuracy and robustness for challenging sequential image collections.

翻译：在本文中,我们展示了一种强大而高效的结构,通过利用照相机从视觉-自然观察仪中提供信息,在具有挑战性的环境中进行准确的三维重建。具体地说,我们提出了一个几何核查方法,通过考虑候选图像配对先前的几何配置来消除不匹配。此外,我们引入了高效和可扩展的重建方法,该方法依赖于分批图像登记和稳健的捆绑调整,两者都利用了可靠的当地odhode估计。广泛的实验结果显示,我们的管道在重建准确性和稳健性以挑战相继图像采集方面比最新的SfM方法表现得更好。