Dashboard cameras (dashcams) record millions of driving videos daily, offering a valuable potential data source for various applications, including driving map production and updates. A necessary step for utilizing these dashcam data involves the estimation of camera poses. However, the low-quality images captured by dashcams, characterized by motion blurs and dynamic objects, pose challenges for existing image-matching methods in accurately estimating camera poses. In this study, we propose a precise pose estimation method for dashcam images, leveraging the inherent camera motion prior. Typically, image sequences captured by dash cameras exhibit pronounced motion prior, such as forward movement or lateral turns, which serve as essential cues for correspondence estimation. Building upon this observation, we devise a pose regression module aimed at learning camera motion prior, subsequently integrating these prior into both correspondences and pose estimation processes. The experiment shows that, in real dashcams dataset, our method is 22% better than the baseline for pose estimation in AUC5\textdegree, and it can estimate poses for 19% more images with less reprojection error in Structure from Motion (SfM).

翻译：行车记录仪每日记录数百万驾驶视频，为各类应用（包括驾驶地图制作与更新）提供了宝贵的潜在数据源。利用这些行车记录仪数据的关键步骤涉及相机位姿的估计。然而，行车记录仪捕获的图像质量较低，存在运动模糊和动态物体等问题，这给现有图像匹配方法在精确估计相机位姿方面带来了挑战。本研究提出一种针对行车记录仪图像的精确位姿估计方法，该方法利用了相机固有的运动先验。通常，行车记录仪捕获的图像序列表现出显著的运动先验，例如前向运动或横向转弯，这些先验为对应点估计提供了关键线索。基于这一观察，我们设计了一个位姿回归模块，旨在学习相机运动先验，随后将这些先验整合到对应点估计和位姿估计过程中。实验表明，在真实行车记录仪数据集中，本方法在位姿估计的AUC5°指标上比基线方法提升22%，并且在运动恢复结构（SfM）中能以更小的重投影误差为额外19%的图像估计位姿。