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

• URL: http://arxiv.org/abs/2301.12135v1
• Date: Sat, 28 Jan 2023 09:06:50 GMT
• Title: AdaSfM: From Coarse Global to Fine Incremental Adaptive Structure from Motion
• Authors: Yu Chen, Zihao Yu, Shu Song, Tianning Yu, Jianming Li, Gim Hee Lee
• Abstract summary: AdaSfM is 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. Our approach uses a threshold-adaptive strategy to align all local reconstructions to the coordinate frame of global SfM.
• Score: 48.835456049755166
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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