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

StructDepth: Leveraging the structural regularities for self-supervised indoor depth estimation