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

AdaSfM: From Coarse Global to Fine Incremental Adaptive Structure from Motion