论文标题
一种可靠的在线方法，用于联合估计焦距和摄像机旋转
A Reliable Online Method for Joint Estimation of Focal Length and Camera Rotation
论文作者
论文摘要
源自建筑环境的规律性的线性透视图可用于在线重新校准内在的和外在的摄像机参数，但是由于场景中的不规则性，在线段估计和背景混乱中的不确定性，这些估计值可能是不可靠的。在这里，我们通过四个计划来应对这一挑战。首先，我们使用PanoContext全景图数据集[27]在广泛的场景，焦距和相机姿势上策划了平面投影的新颖而逼真的数据集。其次，我们使用这个新颖的数据集和YorkurbandB [4]来系统地评估文献中经常发现的线性透视偏差度量，并表明偏差度量和可能性模型的选择对可靠性具有巨大的影响。第三，我们使用这些发现来创建一个用于在线摄像机校准的新型系统，我们称之为fr，并表明它的表现优于先前的最新状态，从而大大减少了估计的摄像机旋转和焦距的错误。我们的第四个贡献是一种新颖有效的方法来估计不确定性，可以通过战略性地选择用于重新校准的框架来大大提高对关键性能应用的在线可靠性。
Linear perspectivecues deriving from regularities of the built environment can be used to recalibrate both intrinsic and extrinsic camera parameters online, but these estimates can be unreliable due to irregularities in the scene, uncertainties in line segment estimation and background clutter. Here we address this challenge through four initiatives. First, we use the PanoContext panoramic image dataset [27] to curate a novel and realistic dataset of planar projections over a broad range of scenes, focal lengths and camera poses. Second, we use this novel dataset and the YorkUrbanDB [4] to systematically evaluate the linear perspective deviation measures frequently found in the literature and show that the choice of deviation measure and likelihood model has a huge impact on reliability. Third, we use these findings to create a novel system for online camera calibration we call fR, and show that it outperforms the prior state of the art, substantially reducing error in estimated camera rotation and focal length. Our fourth contribution is a novel and efficient approach to estimating uncertainty that can dramatically improve online reliability for performance-critical applications by strategically selecting which frames to use for recalibration.
