  Localizing the camera in a known indoor environment is a key building block
for scene mapping, robot navigation, AR, etc. Recent advances estimate the
camera pose via optimization over the 2D/3D-3D correspondences established
between the coordinates in 2D/3D camera space and 3D world space. Such a
mapping is estimated with either a convolution neural network or a decision
tree using only the static input image sequence, which makes these approaches
vulnerable to dynamic indoor environments that are quite common yet challenging
in the real world. To address the aforementioned issues, in this paper, we
propose a novel outlier-aware neural tree which bridges the two worlds, deep
learning and decision tree approaches. It builds on three important blocks: (a)
a hierarchical space partition over the indoor scene to construct the decision
tree; (b) a neural routing function, implemented as a deep classification
network, employed for better 3D scene understanding; and (c) an outlier
rejection module used to filter out dynamic points during the hierarchical
routing process. Our proposed algorithm is evaluated on the RIO-10 benchmark
developed for camera relocalization in dynamic indoor environments. It achieves
robust neural routing through space partitions and outperforms the
state-of-the-art approaches by around 30% on camera pose accuracy, while
running comparably fast for evaluation.
