S2R-DepthNet: Learning a Generalizable Depth-specific Structural Representation (2104.00877v2)

Abstract: Human can infer the 3D geometry of a scene from a sketch instead of a realistic image, which indicates that the spatial structure plays a fundamental role in understanding the depth of scenes. We are the first to explore the learning of a depth-specific structural representation, which captures the essential feature for depth estimation and ignores irrelevant style information. Our S2R-DepthNet (Synthetic to Real DepthNet) can be well generalized to unseen real-world data directly even though it is only trained on synthetic data. S2R-DepthNet consists of: a) a Structure Extraction (STE) module which extracts a domaininvariant structural representation from an image by disentangling the image into domain-invariant structure and domain-specific style components, b) a Depth-specific Attention (DSA) module, which learns task-specific knowledge to suppress depth-irrelevant structures for better depth estimation and generalization, and c) a depth prediction module (DP) to predict depth from the depth-specific representation. Without access of any real-world images, our method even outperforms the state-of-the-art unsupervised domain adaptation methods which use real-world images of the target domain for training. In addition, when using a small amount of labeled real-world data, we achieve the state-ofthe-art performance under the semi-supervised setting. The code and trained models are available at https://github.com/microsoft/S2R-DepthNet.

• The paper introduces S2R-DepthNet, a framework that learns a domain-invariant structural representation for monocular depth estimation using synthetic data.
• The methodology comprises distinct modules for structure extraction, depth-specific attention, and depth prediction to tackle domain shifts effectively.
• Experiments on KITTI and NYU Depth v2 show that the approach outperforms state-of-the-art methods in metrics like Abs-Rel and RMSE.

Overview of S2R-DepthNet: Learning a Generalizable Depth-specific Structural Representation

The paper "S2R-DepthNet: Learning a Generalizable Depth-specific Structural Representation" introduces a novel approach to monocular depth estimation, a critical task in computer vision with applications ranging from autonomous driving to robot navigation. This task involves predicting depth values from a single RGB image, a challenge compounded by the scarcity and costliness of obtaining annotated datasets.

Methodology and Contributions

The core contribution of this work is the development of S2R-DepthNet (Synthetic to Real DepthNet), a framework designed to learn a generalizable depth-specific structural representation that can operate effectively across domain shifts, specifically from synthetic to real-world data. The architecture of S2R-DepthNet comprises three main modules:

1. Structure Extraction (STE) Module: This module extracts a domain-invariant structural representation from images by disentangling them into domain-invariant structure and domain-specific style components. The disentanglement leverages techniques inspired by advanced image translation frameworks, ensuring that the structured information vital for depth estimation is retained while minimizing the impact of stylistic differences across domains.
2. Depth-specific Attention (DSA) Module: The DSA module refines the extracted structural representation by focusing on depth-specific features and suppressing irrelevant structures. This enhancement is achieved through task-specific learning mechanisms that encode high-level semantic knowledge necessary for accurate depth prediction.
3. Depth Prediction Module (DP): Utilizing the refined depth-specific structural representation, this module performs the actual depth estimation. The architecture allows for direct generalization to real-world scenarios without the necessity for real-world training images, addressing a significant limitation in contemporary depth estimation systems.

Robustness and Performance

The paper claims that S2R-DepthNet, when only trained on synthetic datasets, outperforms state-of-the-art unsupervised domain adaptation methods that require real-world images for training. Quantitatively, strong improvements in depth estimation metrics are reported when tested on datasets such as KITTI and NYU Depth v2, highlighting the method's robustness. Specifically, S2R-DepthNet showcases superior performance in terms of Abs-Rel and RMSE metrics compared to existing approaches.

Implications and Future Directions

The implications of this work are substantial for both practical applications and theoretical advancement in domain generalization within computer vision. Practically, S2R-DepthNet reduces the dependency on extensive real-world data collection and annotation, thereby providing an efficient alternative for depth estimation tasks across various fields.

Theoretically, the framework opens avenues for further exploration into the separation of structural and stylistic features in deep learning models, fostering enhanced transferability and generalization across domains. Future research might explore application of similar concepts to other vision tasks where domain shifts pose significant challenges.

In conclusion, S2R-DepthNet is a significant step toward solving domain generalization in depth estimation. Its approach in leveraging synthetic data and domain-invariant structures reflects a promising direction for achieving robust, versatile, and efficient depth prediction frameworks in the evolving landscape of computer vision.