StructDepth: Leveraging the structural regularities for self-supervised indoor depth estimation (2108.08574v1)

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 .

• The paper introduces a novel self-supervised method that integrates Manhattan normal and co-planar constraints to enhance indoor depth estimation.
• It leverages structural regularities to guide learning and mitigate challenges in texture-less regions.
• Experiments on NYU-v2, ScanNet, and InteriorNet demonstrate superior performance over existing state-of-the-art techniques.

An Overview of "StructDepth: Leveraging the Structural Regularities for Self-Supervised Indoor Depth Estimation"

The paper "StructDepth: Leveraging the Structural Regularities for Self-Supervised Indoor Depth Estimation" presents a novel approach to improve self-supervised monocular depth estimation specifically for indoor environments by incorporating structural regularities. This method is particularly meaningful due to the challenges indoor scenes present, such as texture-less regions that degrade traditional self-supervised learning approaches.

Methodology

The core innovation of this work lies in the introduction of two additional supervisory signals: the Manhattan normal constraint and the co-planar constraint, both derived from structural regularities commonly found in indoor architectures. These additions aim to mitigate the intrinsic challenges of texture-less regions:

1. Manhattan Normal Constraint: This constraint is based on the assumption that major surfaces in indoor environments (e.g., floors, ceilings, walls) align with dominant orthogonal directions, known as the Manhattan-world model. By penalizing deviations from these dominant directions, this approach reinforces the network to learn depth in alignment with structural characteristics.
2. Co-planar Constraint: This constraint enforces that sets of 3D points within the same planar region should lie on a common plane. During training, planar region detections are iteratively improved as the depth estimates become more accurate, forming a feedback loop that enhances model performance over time.

The methodological framework involves dynamically generated supervisory signals through adaptive detection mechanisms for both the Manhattan normals and planar regions. These signals, although noisy in initial epochs, self-improve in synergy with the depth predictions.

Results

The experimental evaluation carried out on datasets such as NYU-v2, ScanNet, and InteriorNet highlights that the proposed model outperforms existing state-of-the-art self-supervised methods in indoor environments. Notably, the paper reports improved results over previous approaches such as Monodepth2 and P\textsuperscript{2}Net in terms of RMS, AbsRel, and other standard metrics used in depth estimation tasks.

Implications and Future Directions

The implications of this work are both practical and theoretical. From a practical standpoint, this approach alleviates the data constraints in indoor depth estimation by obviating the need for extensive ground-truth datasets that are often laborious to obtain. Theoretically, this work reinforces the effectiveness of integrating structural regularities into neural network training, which may inspire further exploration into domain-specific priors across other challenging environments.

The future trajectory in self-supervised depth estimation could incorporate more sophisticated geometric priors or explore adaptive weighting schemes that further balance different supervisory signals depending on scene context. Moreover, expanding the applicability of these concepts to outdoor scenes or non-Manhattan structured environments could represent significant advancements in the field.

The provision of publicly available source code underscores the authors' commitment to facilitating further research and development in this domain. The successful deployment and generalization of this methodology may pave the way for more robust hybrid training frameworks that exploit both data and model-driven insights.