VIO-Aided Structure from Motion Under Challenging Environments
This work addresses the need for reliable 3D reconstruction in difficult conditions, such as sequential image collections, but appears incremental as it builds on existing SfM and VIO techniques.
The paper tackles the problem of accurate 3D reconstruction in challenging environments by integrating visual-inertial odometry (VIO) into a Structure from Motion pipeline, resulting in improved reconstruction accuracy and robustness compared to state-of-the-art methods.
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.