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

TL;DR

This paper introduces StructDepth, a self-supervised indoor depth estimation method that leverages structural regularities like Manhattan constraints and planar regions to improve accuracy in texture-scarce indoor environments.

Contribution

It proposes novel supervisory signals based on indoor structural regularities, enhancing self-supervised depth estimation performance in indoor scenes.

Findings

Outperforms state-of-the-art indoor depth estimation methods

Effective use of Manhattan and co-planar constraints improves accuracy

Demonstrates robustness in texture-scarce indoor environments

Abstract

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 .