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

TL;DR

AdaSfM introduces a scalable, robust, and accurate structure from motion method that combines coarse global estimation with fine local refinement, effectively handling large-scale scenes with outliers and sparse views.

Contribution

The paper presents a novel coarse-to-fine adaptive SfM framework that integrates global and local methods for improved accuracy and efficiency on large datasets.

Findings

Achieves state-of-the-art accuracy on large-scale benchmarks.

Demonstrates robustness to outliers and sparse view graphs.

Improves computational efficiency compared to existing methods.

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.