论文标题
照明不变的主动摄像机重新定位，以便在野外进行细粒度变化检测
Illumination-Invariant Active Camera Relocalization for Fine-Grained Change Detection in the Wild
论文作者
论文摘要
主动摄像机重新定位（ACR）是计算机视觉中的一个新问题，它大大降低了由于摄像头姿势未对准在细粒度变化检测（FGCD）中引起的图像扭曲引起的错误警报。尽管ACR可以支持成果，但它仍然是一个充满挑战的问题，这是由于相对姿势估计的不稳定结果，尤其是对于室外场景，在室外场景中，照明条件无法控制，即两次观察结果可能具有高度不同的照明。本文研究了一种照明不变的主动摄像头重新定位方法，它在相对姿势估计和尺度估计中都改进了。我们使用平面段作为中间表示，以促进特征匹配，从而进一步提高照明方差下的姿势估计鲁棒性和可靠性。此外，我们构建了一个线性系统，以最大程度地减少图像扭曲误差，从而在每个ACR迭代中获得绝对尺度，从而大大减少了ACR过程的时间消耗，它比最先进的ACR策略要快$ 1.6 $倍。我们的工作大大扩展了现实世界中的细粒度变更监测任务的可行性。广泛的实验测试和现实世界应用验证了使用ACR任务的拟议姿势估计方法的有效性和鲁棒性。
Active camera relocalization (ACR) is a new problem in computer vision that significantly reduces the false alarm caused by image distortions due to camera pose misalignment in fine-grained change detection (FGCD). Despite the fruitful achievements that ACR can support, it still remains a challenging problem caused by the unstable results of relative pose estimation, especially for outdoor scenes, where the lighting condition is out of control, i.e., the twice observations may have highly varied illuminations. This paper studies an illumination-invariant active camera relocalization method, it improves both in relative pose estimation and scale estimation. We use plane segments as an intermediate representation to facilitate feature matching, thus further boosting pose estimation robustness and reliability under lighting variances. Moreover, we construct a linear system to obtain the absolute scale in each ACR iteration by minimizing the image warping error, thus, significantly reduce the time consume of ACR process, it is nearly $1.6$ times faster than the state-of-the-art ACR strategy. Our work greatly expands the feasibility of real-world fine-grained change monitoring tasks for cultural heritages. Extensive experiments tests and real-world applications verify the effectiveness and robustness of the proposed pose estimation method using for ACR tasks.
