Abstract
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.
Abstract (translated)
尽管许多现有的结构从运动(SfM)方法取得了令人印象深刻的结果,但在大量异步匹配和视图 Graph 稀疏的大型场景中,仍然需要改善鲁棒性、精度和效率。在本文中,我们提出了 AdaSfM:一种粗到细的自适应 SfM 方法,可以扩展到大规模和挑战性的数据集。我们的方法首先进行粗的全球 SfM,通过利用低成本传感器如惯性测量单元(IMUs)和车轮编码器来提高视图 Graph 的可靠性。随后,视图 Graph 被分解成子场景,通过 fine 局部增量 SfM regularized 的粗的全球 SfM 结果并行 refined,以提高相机注册精度并减轻场景漂移。最后,我们的方法使用阈值自适应策略将所有局部重构对齐到全球 SfM 的坐标系。对大规模基准数据集的广泛实验表明,我们的方法实现了先进的精度和效率。
URL
https://arxiv.org/abs/2301.12135
