Despite the impressive results achieved by many existing Structure from Motion (SfM) approaches, there is still a need to improve the robustness, accuracy, and efficiency on large-scale scenes with many outlier matches and sparse view graphs. In this paper, we propose AdaSfM: a coarse-to-fine adaptive SfM approach that is scalable to large-scale and challenging datasets. Our approach first does a coarse global SfM which improves the reliability of the view graph by leveraging measurements from low-cost sensors such as Inertial Measurement Units (IMUs) and wheel encoders. Subsequently, the view graph is divided into sub-scenes that are refined in parallel by a fine local incremental SfM regularised by the result from the coarse global SfM to improve the camera registration accuracy and alleviate scene drifts. Finally, our approach uses a threshold-adaptive strategy to align all local reconstructions to the coordinate frame of global SfM. Extensive experiments on large-scale benchmark datasets show that our approach achieves state-of-the-art accuracy and efficiency.

翻译：尽管现有许多运动恢复结构（Structure from Motion, SfM）方法已取得显著成果，但在处理包含大量外点匹配和稀疏视图图的大规模场景时，其鲁棒性、精度和效率仍有待提升。本文提出AdaSfM：一种从粗到细的自适应SfM方法，可扩展至大规模和具有挑战性的数据集。该方法首先通过利用低成本传感器（如惯性测量单元和轮式编码器）的测量结果进行粗粒度全局SfM，从而提高视图图的可靠性。随后，将视图图划分为若干子场景，并通过细粒度局部增量SfM并行优化，同时以粗粒度全局SfM结果作为正则化约束，以提升相机配准精度并减轻场景漂移。最后，采用阈值自适应策略将所有局部重建结果对齐到全局SfM的坐标系。在大规模基准数据集上的广泛实验表明，本方法实现了最先进的精度与效率。