Papers › Gravity-aligned Rotation Averaging with Circular Regression
Gravity-aligned Rotation Averaging with Circular Regression
Linfei Pan, Marc Pollefeys, Dániel Baráth
Reconstructing a 3D scene from unordered images is pivotal in computer vision and robotics, with applications spanning crowd-sourced mapping and beyond. While global Structure-from-Motion (SfM) techniques are scalable and fast, they often compromise on accuracy. To address this, we introduce a principled approach that integrates gravity direction into the rotation averaging phase of global pipelines, enhancing camera orientation accuracy and reducing the degrees of freedom. This additional information is commonly available in recent consumer devices, such as smartphones, mixed-reality devices and drones, making the proposed method readily accessible. Rooted in circular regression, our algorithm has similar convergence guarantees as linear regression. It also supports scenarios where only a subset of cameras have known gravity. Additionally, we propose a mechanism to refine error-prone gravity. We achieve state-of-the-art accuracy on four large-scale datasets. Particularly, the proposed method improves upon the SfM baseline by 13 AUC@1° points, on average, while running eight times faster. It also outperforms the standard planar pose graph optimization technique by 23 AUC@1° points. The code is at https://github.com/colmap/glomap.
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