Single View Stereo Matching (1803.02612v2)

Abstract: Previous monocular depth estimation methods take a single view and directly regress the expected results. Though recent advances are made by applying geometrically inspired loss functions during training, the inference procedure does not explicitly impose any geometrical constraint. Therefore these models purely rely on the quality of data and the effectiveness of learning to generalize. This either leads to suboptimal results or the demand of huge amount of expensive ground truth labelled data to generate reasonable results. In this paper, we show for the first time that the monocular depth estimation problem can be reformulated as two sub-problems, a view synthesis procedure followed by stereo matching, with two intriguing properties, namely i) geometrical constraints can be explicitly imposed during inference; ii) demand on labelled depth data can be greatly alleviated. We show that the whole pipeline can still be trained in an end-to-end fashion and this new formulation plays a critical role in advancing the performance. The resulting model outperforms all the previous monocular depth estimation methods as well as the stereo block matching method in the challenging KITTI dataset by only using a small number of real training data. The model also generalizes well to other monocular depth estimation benchmarks. We also discuss the implications and the advantages of solving monocular depth estimation using stereo methods.

• The paper reformulates monocular depth estimation into a two-step process: view synthesis followed by stereo matching to explicitly enforce geometric constraints.
• It introduces an end-to-end trainable pipeline that minimizes dependency on large labeled datasets while improving accuracy on challenging benchmarks.
• Experimental results demonstrate that the approach outperforms traditional methods, offering efficient and competitive performance with limited real-world data.

Overview of Single View Stereo Matching

In the paper "Single View Stereo Matching," Luo et al. propose a novel approach to monocular depth estimation by reformulating it into a two-step process—view synthesis followed by stereo matching. This paper addresses the limitations of previous monocular depth estimation methods, which rely heavily on large amounts of labeled data and struggle to incorporate geometric constraints during inference.

The authors argue that monocular depth estimation can benefit from reinterpreting it as a stereo matching task with two sub-problems: generating a synthetic right view from a single input image and conducting stereo matching between the original and generated view. This approach allows for explicit geometric constraints during inference, potentially reducing dependency on labeled datasets. The paper outlines how the process can be trained end-to-end while maintaining performance superior to existing methods.

Key Contributions

1. Reformulation of the Problem: The research highlights an innovative perspective by decoupling monocular depth estimation into view synthesis and stereo matching tasks. This partition respects geometric constraints, which are often overlooked in previous solutions.
2. End-to-End Learning Pipeline: The proposed solution is trainable in an end-to-end manner and avoids the need for large amounts of labeled depth data, achieving better generalization.
3. Benchmark Results: The model not only surpasses traditional monocular methods but also achieves competitive results against stereo block matching methods on the KITTI dataset, using fewer real-world training samples.

Numerical Performance and Implications

The paper demonstrates substantial improvement over existing methods in benchmarks for depth estimation, leveraging enhanced geometric reasoning capabilities through structured end-to-end training. Specifically, the model achieves state-of-the-art accuracy on the challenging KITTI dataset and performs comparably to stereo matching methods, even when utilizing a monocular setup.

The experimental results suggest that even with a limited number of real-world samples, the proposed framework can outperform unsupervised and semi-supervised approaches. This finding indicates potential efficiency gain in applications where data collection is expensive or impractical.

Future Developments

Potential future directions could further explore enhancements in view synthesis algorithms and stereo matching networks to bolster geometric accuracy and computational efficiency. Additionally, applying this modular framework to other computer vision tasks might reveal broader applications where explicit geometric constraints can be beneficial.

The paper lays the groundwork for refining monocular depth estimation by moving closer to the performance levels of traditional stereo matching systems while simplifying data requirements. Further research should focus on integrating this approach into broader vision systems, like real-time autonomous navigation, where depth perception is critical.