论文标题
基于图像的摄像头姿势估计技术的批判性分析
A Critical Analysis of Image-based Camera Pose Estimation Techniques
论文作者
论文摘要
摄像机以及与视野内的对象相关联，本地化可以使许多计算机视野字段（例如自动驾驶，机器人导航和增强现实（AR））受益。在此调查中，我们首先介绍特定的应用领域和相机定位的评估指标，该指标根据不同的子任务（基于学习的2D-2D任务，基于功能的2D-3D任务和3D-3D任务）。然后，我们通过对方法进行认真建模的方法来启发其算法的进一步改进，例如损失函数，神经网络结构，从而回顾基于结构的摄像头姿势估计方法，绝对姿势回归和相对姿势回归方法的常见方法。此外，我们总结了用于相机本地化的流行数据集是什么，并将这些方法的定量和定性结果与详细的性能指标进行比较。最后，我们讨论未来的研究可能性和应用。
Camera, and associated with its objects within the field of view, localization could benefit many computer vision fields, such as autonomous driving, robot navigation, and augmented reality (AR). In this survey, we first introduce specific application areas and the evaluation metrics for camera localization pose according to different sub-tasks (learning-based 2D-2D task, feature-based 2D-3D task, and 3D-3D task). Then, we review common methods for structure-based camera pose estimation approaches, absolute pose regression and relative pose regression approaches by critically modelling the methods to inspire further improvements in their algorithms such as loss functions, neural network structures. Furthermore, we summarise what are the popular datasets used for camera localization and compare the quantitative and qualitative results of these methods with detailed performance metrics. Finally, we discuss future research possibilities and applications.
