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Temporal camera relocalization estimates the pose with respect to each videoframe in sequence, as opposed to one-shot relocalization which focuses on astill image. Even though the time dependency has been taken into account,current temporal relocalization methods still generally underperform thestate-of-the-art one-shot approaches in terms of accuracy. In this work, weimprove the temporal relocalization method by using a network architecture thatincorporates Kalman filtering (KFNet) for online camera relocalization. Inparticular, KFNet extends the scene coordinate regression problem to the timedomain in order to recursively establish 2D and 3D correspondences for the posedetermination. The network architecture design and the loss formulation arebased on Kalman filtering in the context of Bayesian learning. Extensiveexperiments on multiple relocalization benchmarks demonstrate the high accuracyof KFNet at the top of both one-shot and temporal relocalization approaches.Our codes are released at https://github.com/zlthinker/KFNet.

KFNet: Learning Temporal Camera Relocalization using Kalman Filtering