  Self-supervised monocular depth estimation has achieved impressive
performance on outdoor datasets. Its performance however degrades notably in
indoor environments because of the lack of textures. Without rich textures, the
photometric consistency is too weak to train a good depth network. Inspired by
the early works on indoor modeling, we leverage the structural regularities
exhibited in indoor scenes, to train a better depth network. Specifically, we
adopt two extra supervisory signals for self-supervised training: 1) the
Manhattan normal constraint and 2) the co-planar constraint. The Manhattan
normal constraint enforces the major surfaces (the floor, ceiling, and walls)
to be aligned with dominant directions. The co-planar constraint states that
the 3D points be well fitted by a plane if they are located within the same
planar region. To generate the supervisory signals, we adopt two components to
classify the major surface normal into dominant directions and detect the
planar regions on the fly during training. As the predicted depth becomes more
accurate after more training epochs, the supervisory signals also improve and
in turn feedback to obtain a better depth model. Through extensive experiments
on indoor benchmark datasets, the results show that our network outperforms the
state-of-the-art methods. The source code is available at
https://github.com/SJTU-ViSYS/StructDepth .
