  Camera relocalization plays a vital role in many robotics and computer vision
tasks, such as global localization, recovery from tracking failure, and loop
closure detection. Recent random forests based methods directly predict 3D
world locations for 2D image locations to guide the camera pose optimization.
During training, each tree greedily splits the samples to minimize the spatial
variance. However, these greedy splits often produce uneven sub-trees in
training or incorrect 2D-3D correspondences in testing. To address these
problems, we propose a sample-balanced objective to encourage equal numbers of
samples in the left and right sub-trees, and a novel backtracking scheme to
remedy the incorrect 2D-3D correspondence predictions. Furthermore, we extend
the regression forests based methods to use local features in both training and
testing stages for outdoor RGB-only applications. Experimental results on
publicly available indoor and outdoor datasets demonstrate the efficacy of our
approach, which shows superior or on-par accuracy with several state-of-the-art
methods.
