  Self-supervised depth estimation for indoor environments is more challenging
than its outdoor counterpart in at least the following two aspects: (i) the
depth range of indoor sequences varies a lot across different frames, making it
difficult for the depth network to induce consistent depth cues, whereas the
maximum distance in outdoor scenes mostly stays the same as the camera usually
sees the sky; (ii) the indoor sequences contain much more rotational motions,
which cause difficulties for the pose network, while the motions of outdoor
sequences are pre-dominantly translational, especially for driving datasets
such as KITTI. In this paper, special considerations are given to those
challenges and a set of good practices are consolidated for improving the
performance of self-supervised monocular depth estimation in indoor
environments. The proposed method mainly consists of two novel modules, \ie, a
depth factorization module and a residual pose estimation module, each of which
is designed to respectively tackle the aforementioned challenges. The
effectiveness of each module is shown through a carefully conducted ablation
study and the demonstration of the state-of-the-art performance on three indoor
datasets, \ie, EuRoC, NYUv2, and 7-scenes.
