R-SCoRe: Revisiting Scene Coordinate Regression for Robust Large-Scale Visual Localization (2501.01421v2)

Abstract: Learning-based visual localization methods that use scene coordinate regression (SCR) offer the advantage of smaller map sizes. However, on datasets with complex illumination changes or image-level ambiguities, it remains a less robust alternative to feature matching methods. This work aims to close the gap. We introduce a covisibility graph-based global encoding learning and data augmentation strategy, along with a depth-adjusted reprojection loss to facilitate implicit triangulation. Additionally, we revisit the network architecture and local feature extraction module. Our method achieves state-of-the-art on challenging large-scale datasets without relying on network ensembles or 3D supervision. On Aachen Day-Night, we are 10$\times$ more accurate than previous SCR methods with similar map sizes and require at least 5$\times$ smaller map sizes than any other SCR method while still delivering superior accuracy. Code is available at: https://github.com/cvg/scrstudio .

• The paper introduces R-SCoRe, a refined scene coordinate regression (SCR) framework to improve the robustness and efficiency of visual localization in large-scale, complex environments.
• A novel depth-adjusted reprojection loss is proposed to counter the network's bias towards distant points and improve training stability without direct 3D supervision.
• R-SCoRe achieves state-of-the-art performance comparable to feature matching methods on challenging benchmarks while significantly reducing map sizes to as low as 47MB.

Revisiting Scene Coordinate Regression for Robust Large-Scale Visual Localization

The paper "R-SCoRe: Revisiting Scene Coordinate Regression for Robust Large-Scale Visual Localization" investigates the enhancement of learning-based visual localization techniques that utilize scene coordinate regression (SCR). Traditionally, SCR methods have suffered from robustness issues when applied to datasets featuring complex illumination changes or ambiguities at the image level. The authors present a comprehensive strategy to address these challenges, thereby elevating the performance of SCR within large-scale and intricate environments.

Summary of Contributions

The paper introduces a refined SCR framework, termed R-SCoRe, that incorporates several innovative methodologies to improve localization performance while significantly reducing map sizes. The key contributions include:

1. Covisibility Graph-based Encoding: The authors employ a global encoding learning method based on a covisibility graph. This methodology is intended to enhance data augmentation strategies and global encoding. Node embeddings are learned using Node2Vec, which enables an efficient exploration of the scene graph, thereby aligning training data more effectively with the scene's visibility structure.
2. Depth-adjusted Reprojection Loss: A new reprojection loss adjusted by the depth of predicted scene coordinates is introduced. This adjustment aims to correct the network's inherent bias towards distant points, which typically demonstrates a lower reprojection error in standard SCR pipelines. The depth-adjusted loss promotes accurate localization even without relying on direct 3D coordinate supervision.
3. Refined Network Architecture: The paper proposes enhancements to the neural architecture, including the addition of a refinement module. This module predicts both coarse and refined outputs, which improves the training stability and convergence, ensuring that more accurate 3D point estimations are achieved.
4. Pretrained Feature Extractors for Local Encodings: Without the need for large-scale scene representations, pretrained dense and sparse feature extractors such as LoFTR and Dedode are employed for encoding local information. This not only reduces memory consumption but also improves the discrimination power of the feature representations.

These strategic improvements render R-SCoRe competitive with traditional feature matching methods while requiring significantly smaller maps, as low as 47MB, which is an order of magnitude more efficient than most other SCR methods.

Implications and Future Work

The practical implications of R-SCoRe are notable, particularly in applications where map size constraints are critical, such as augmented/virtual reality, autonomous navigation, and robotics. The authors demonstrate that SCR can not only be used as a viable alternative to feature matching but also excel in conditions previously deemed challenging for SCR methodologies.

While R-SCoRe marks a substantial methodological leap, it still exhibits some performance gap relative to the highest accuracy thresholds achieved by feature matching techniques. This leaves room for further research into improving network generalizability and integration with other models. An interesting avenue of future research could explore the fusion of R-SCoRe with generative models like Neural Radiance Fields (NeRF). Hybrid approaches could potentially leverage the initial SCR estimates and refine them through generational adjustments, aligning directly with extensive view synthesis capabilities.

Moreover, the pipeline could be optimized further by integrating neural network compression techniques such as pruning, quantization, and low-rank approximations to reduce computational overhead while maintaining accuracy, thereby making R-SCoRe more feasible for real-time applications.

In conclusion, R-SCoRe demonstrates significant advancements in the SCR framework, achieving state-of-the-art results on complex, large-scale benchmarks. As researchers continue to refine these techniques, extending their application to even broader scenarios becomes a promising possibility.