Rethinking Optical Flow from Geometric Matching Consistent Perspective (2303.08384v1)

Abstract: Optical flow estimation is a challenging problem remaining unsolved. Recent deep learning based optical flow models have achieved considerable success. However, these models often train networks from the scratch on standard optical flow data, which restricts their ability to robustly and geometrically match image features. In this paper, we propose a rethinking to previous optical flow estimation. We particularly leverage Geometric Image Matching (GIM) as a pre-training task for the optical flow estimation (MatchFlow) with better feature representations, as GIM shares some common challenges as optical flow estimation, and with massive labeled real-world data. Thus, matching static scenes helps to learn more fundamental feature correlations of objects and scenes with consistent displacements. Specifically, the proposed MatchFlow model employs a QuadTree attention-based network pre-trained on MegaDepth to extract coarse features for further flow regression. Extensive experiments show that our model has great cross-dataset generalization. Our method achieves 11.5% and 10.1% error reduction from GMA on Sintel clean pass and KITTI test set. At the time of anonymous submission, our MatchFlow(G) enjoys state-of-the-art performance on Sintel clean and final pass compared to published approaches with comparable computation and memory footprint. Codes and models will be released in https://github.com/DQiaole/MatchFlow.

• The paper introduces geometric image matching pre-training to leverage large-scale real-world data and enhance optical flow robustness.
• It implements the MatchFlow model with a QuadTree attention network, achieving up to 11.5% error reduction on Sintel and 10.1% on KITTI.
• The study challenges traditional optical flow paradigms, offering insights that benefit applications like video interpolation and action recognition.

Rethinking Optical Flow from a Geometric Matching Consistent Perspective

The paper "Rethinking Optical Flow from a Geometric Matching Consistent Perspective" proposes a novel approach to optical flow estimation by integrating Geometric Image Matching (GIM) as a pre-training phase. This work challenges the conventional methodologies of training optical flow models by suggesting an alternative that enhances the robustness and accuracy of the optical flow estimation process. Through the paper, the authors present the MatchFlow model, which significantly improves optical flow estimation performance with reduced error rates in cross-dataset evaluations.

Key Contributions

1. Geometric Image Matching as Pre-Training: The paper highlights the limitations of existing deep learning models on optical flow, primarily trained from scratch using standard datasets. The authors introduce GIM as a pre-training task to leverage massive labeled real-world data. This approach aims to learn fundamental feature correlations between scenes and objects, enhancing the computed optical flow's robustness against large displacements and appearance changes.
2. MatchFlow Model Implementation: The MatchFlow employs a QuadTree attention-based network pre-trained on the MegaDepth dataset to extract coarse features that aid in accurate flow regression. This architecture focuses on an iterative refinement mechanism incorporating QuadTree attention blocks for better feature representation.
3. Empirical Evaluations: Extensive experiments validate the model's capability to reduce error rates significantly. MatchFlow achieves notable improvements, such as an 11.5% error reduction from the GMA baseline on the Sintel clean pass and a 10.1% reduction on the KITTI test set. These results position MatchFlow as a state-of-the-art performer in optical flow estimation tasks.
4. Theoretical and Practical Implications: By reformulating the optical flow estimation pipeline with GIM as a backbone, the paper challenges the existing training paradigms and provides insights that can be extended to other related vision tasks. The reduced error metrics across datasets underscore the method's potential in applications like video frame interpolation and action recognition.

Discussion and Future Directions

The implications of this research are profound in both practical applications and theoretical advancements in computer vision. The integration of GIM in the pre-training phase offers a promising direction for improving the generalization capabilities of optical flow models across diverse datasets. Furthermore, this methodology opens avenues for exploring other domains where feature consistency and robust matching are critical.

Future developments in AI could potentially expand upon this framework, adapting it for higher-dimensional data or integrating it into larger multimodal systems where motion prediction is crucial. Additionally, further improvements in attention mechanisms or alternative matching strategies may continue to enhance model efficacy and streamline computational requirements.

In summary, "Rethinking Optical Flow from a Geometric Matching Consistent Perspective" presents a compelling case for reevaluating traditional optical flow training methods. By harnessing the advantages of GIM pre-training and innovative attention mechanisms, the research offers substantive progress towards more resilient and accurate optical flow models.