In this work, we tackle the problem of active camera localization, which
controls the camera movements actively to achieve an accurate camera pose. The
past solutions are mostly based on Markov Localization, which reduces the
position-wise camera uncertainty for localization. These approaches localize
the camera in the discrete pose space and are agnostic to the
localization-driven scene property, which restricts the camera pose accuracy in
the coarse scale. We propose to overcome these limitations via a novel active
camera localization algorithm, composed of a passive and an active localization
module. The former optimizes the camera pose in the continuous pose space by
establishing point-wise camera-world correspondences. The latter explicitly
models the scene and camera uncertainty components to plan the right path for
accurate camera pose estimation. We validate our algorithm on the challenging
localization scenarios from both synthetic and scanned real-world indoor
scenes. Experimental results demonstrate that our algorithm outperforms both
the state-of-the-art Markov Localization based approach and other compared
approaches on the fine-scale camera pose accuracy. Code and data are released
at this https URL

提出一种新型的主动摄像机定位算法，包括一个被动和一个主动模块，前者通过建立点对点的相机与世界的对应关系，在连续的位姿空间中优化相机位姿，后者则显式地建模场景和相机的不确定性部件以规划精准摄像机位姿估计的路径。