R2D2: Repeatable and Reliable Detector and Descriptor (1906.06195v2)

Abstract: Interest point detection and local feature description are fundamental steps in many computer vision applications. Classical methods for these tasks are based on a detect-then-describe paradigm where separate handcrafted methods are used to first identify repeatable keypoints and then represent them with a local descriptor. Neural networks trained with metric learning losses have recently caught up with these techniques, focusing on learning repeatable saliency maps for keypoint detection and learning descriptors at the detected keypoint locations. In this work, we argue that salient regions are not necessarily discriminative, and therefore can harm the performance of the description. Furthermore, we claim that descriptors should be learned only in regions for which matching can be performed with high confidence. We thus propose to jointly learn keypoint detection and description together with a predictor of the local descriptor discriminativeness. This allows us to avoid ambiguous areas and leads to reliable keypoint detections and descriptions. Our detection-and-description approach, trained with self-supervision, can simultaneously output sparse, repeatable and reliable keypoints that outperforms state-of-the-art detectors and descriptors on the HPatches dataset. It also establishes a record on the recently released Aachen Day-Night localization dataset.

• The paper presents a joint detection and description method that improves keypoint repeatability and matching reliability.
• It utilizes self-supervised learning with novel loss functions to reduce reliance on annotated data while ensuring uniform keypoint coverage.
• Experimental results on HPatches and Aachen Day-Night demonstrate R2D2’s superior performance in visual localization tasks.

An Analysis of R2D2: Repeatable and Reliable Detector and Descriptor

The paper introduces a novel approach known as R2D2, aimed at addressing the critical tasks of interest point detection and local feature description in the computer vision domain. The authors articulate a deviation from the traditional detect-then-describe paradigm by proposing a methodology that jointly learns keypoint detection and description processes, coupled with a predictor of the local descriptor's discriminativeness. This integrated approach promises to obviate ambiguous areas in visual data, thus increasing the reliability of keypoint detection.

Core Contributions

1. Joint Detection and Description: Unlike conventional methods that treat detection and description separately, R2D2 learns these two components together. The paper underscores that detection and description are intertwined, suggesting that good keypoints must be repeatable and reliable for matching.
2. Self-supervised Learning: The authors employ self-supervised learning to train the model. This approach leverages a structural relationship within the data, avoiding the necessity for annotated training data, which significantly reduces the dependency on manual supervision.
3. Novel Loss Functions: A new unsupervised loss encourages both repeatability and sparsity in keypoints while maintaining a uniform coverage across the image. A separate reliability confidence value is also learned, enhancing the discriminativeness of the local descriptors.

Experimental Evaluation

The paper provides robust experimental results demonstrating that R2D2 exceeds state-of-the-art performance benchmarks on the HPatches dataset. It showcases superior detector repeatability and matching scores when compared to other learned and handcrafted approaches. Moreover, R2D2 sets a new benchmark on the Aachen Day-Night localization task, significant for visual localization applications. This performance is attributed to the dual emphasis on keypoint repeatability and descriptor reliability.

Implications and Future Developments

The proposed R2D2 methodology suggests a considerable advancement for tasks requiring precise feature matching, such as visual localization, structure-from-motion, and 3D reconstruction. By mitigating the limitations of independently learned keypoint detectors and descriptors, R2D2 can potentially be adapted to other applications that demand robust keypoint matching.

The approach's reliance on self-supervision indicates a progressive stride towards more autonomous models that do not necessitate labor-intensive data labeling. Future developments could explore the scalability of R2D2 to even larger datasets and its adaptation to more dynamic environments, potentially integrating scene-specific adaptations for enhanced robustness against occlusions and varying lighting conditions.

Conclusion

R2D2 represents a significant step forward in joint learning frameworks for keypoint detection and description. By integrating keypoint repeatability and descriptor reliability, the authors present a compelling case for revisiting how data-driven models approach these foundational tasks in computer vision. The paper stands as a testament to the evolving nature of self-supervised learning where models can significantly outperform traditional and even other learning-based counterparts. The future of this research could align towards more generalized solutions adaptable across diverse visual tasks.