Temporal camera relocalization estimates the pose with respect to each video
frame in sequence, as opposed to one-shot relocalization which focuses on a
still image. Even though the time dependency has been taken into account,
current temporal relocalization methods still generally underperform the
state-of-the-art one-shot approaches in terms of accuracy. In this work, we
improve the temporal relocalization method by using a network architecture that
incorporates Kalman filtering (KFNet) for online camera relocalization. In
particular, KFNet extends the scene coordinate regression problem to the time
domain in order to recursively establish 2D and 3D correspondences for the pose
determination. The network architecture design and the loss formulation are
based on Kalman filtering in the context of Bayesian learning. Extensive
experiments on multiple relocalization benchmarks demonstrate the high accuracy
of KFNet at the top of both one-shot and temporal relocalization approaches.
Our codes are released at this https URL

通过使用 Kalman 滤波网络改进了相机时空重定位方法，实现了极高的定位精度。