Revisiting Stereo Depth Estimation From a Sequence-to-Sequence Perspective with Transformers (2011.02910v4)

Abstract: Stereo depth estimation relies on optimal correspondence matching between pixels on epipolar lines in the left and right images to infer depth. In this work, we revisit the problem from a sequence-to-sequence correspondence perspective to replace cost volume construction with dense pixel matching using position information and attention. This approach, named STereo TRansformer (STTR), has several advantages: It 1) relaxes the limitation of a fixed disparity range, 2) identifies occluded regions and provides confidence estimates, and 3) imposes uniqueness constraints during the matching process. We report promising results on both synthetic and real-world datasets and demonstrate that STTR generalizes across different domains, even without fine-tuning.

• The paper presents STTR, a novel Transformer-based method that uses sequence-to-sequence attention for accurate depth estimation and occlusion handling.
• It enforces a uniqueness constraint combined with optimal transport theory to ensure precise pixel correspondence without a fixed disparity range.
• STTR demonstrates robust cross-domain generalization, achieving competitive results on various datasets without the need for fine-tuning.

Revisiting Stereo Depth Estimation from a Sequence-to-Sequence Perspective with Transformers

The paper "Revisiting Stereo Depth Estimation From a Sequence-to-Sequence Perspective with Transformers" presents an innovative method for addressing stereo depth estimation using Transformer-based architectures. Stereo depth estimation traditionally relies on accurately matching pixel correspondences between stereo image pairs to compute disparity, which is then used to infer depth information about a scene. The paper introduces a novel approach termed the STereo TRansformer (STTR), which leverages the capabilities of Transformers to enhance this process.

Key Innovations

1. Transformer-Based Architecture: Contrary to traditional methods that use cost volume construction, STTR employs a dense sequence-to-sequence pixel matching strategy utilizing attention mechanisms. This eliminates the dependency on a fixed disparity range and instead utilizes the positional information and attention capabilities of Transformers.
2. Handling Disparity and Occlusion: STTR not only provides a solution to the fixed disparity range limitation prevalent in existing methods but also incorporates a mechanism to identify occluded regions and estimate confidence levels. This is achieved through a combination of attention-driven positional encoding and optimal transport theory, allowing for more accurate disparity estimation even in challenging scenarios.
3. Uniqueness Constraint: By incorporating a sequence-to-sequence framework, STTR enforces a uniqueness constraint during the matching process, ensuring that each pixel is matched to exactly one corresponding pixel in the paired image. This constraint enhances the accuracy of the algorithm by minimizing ambiguity during disparity calculations.
4. Cross-Domain Generalization: The STTR model demonstrates significant cross-domain generalization capabilities, achieving competitive results across various datasets without the need for model fine-tuning. This highlights the robustness and adaptability of the proposed architecture.

Performance and Comparisons

The authors conducted extensive evaluations of STTR across both synthetic and real-world datasets, such as Scene Flow, KITTI 2015, and Middlebury 2014. Notably, STTR achieved favorable results compared to existing state-of-the-art methods, particularly excelling in environments with unlimited disparity, where it outperforms competitors by a notable margin. A prominent strength is its ability to generalize across different domains without further training, maintaining performance levels that are competitive with models specifically fine-tuned for those domains.

For instance, the method exhibited a low error rate (3 px error) and proved capable of performing accurate occlusion detection, which previous methods often overlooked. Additionally, the lack of a pre-defined disparity range allows for a more flexible application, aligning well with real-world requirements such as those encountered in autonomous driving and robotic interventions.

Implications and Future Directions

The introduction of Transformers into stereo depth estimation opens up several avenues for future exploration. First, the paper suggests enhanced scalability and flexibility in applications involving large disparity variations, offering the potential to extend towards more complex 3D scene reconstruction tasks. Additionally, the ability of STTR to handle high-resolution images with reasonably low computational overhead democratizes high-accuracy depth sensing across a wider array of accessible hardware platforms.

Furthermore, the application of a Transformer network combined with attention mechanisms could be adapted to other computer vision tasks, implicating a broader impact on disciplines requiring precise spatial information extraction. There might also be potential in further optimizing the network architecture through multi-resolution techniques or incorporating novel training paradigms to account for dynamic environments and evolving datasets.

In sum, this paper makes a substantial contribution to the field of computer vision and sets a foundational precedent for future research leveraging Transformer networks in stereo vision. The provided advancements in tackling occlusion, disparity limitations, and cross-domain adaptability signal a positive shift in developing robust and scalable depth estimation methodologies.