Reloc3r: Large-Scale Training of Relative Camera Pose Regression for Generalizable, Fast, and Accurate Visual Localization (2412.08376v2)

Abstract: Visual localization aims to determine the camera pose of a query image relative to a database of posed images. In recent years, deep neural networks that directly regress camera poses have gained popularity due to their fast inference capabilities. However, existing methods struggle to either generalize well to new scenes or provide accurate camera pose estimates. To address these issues, we present Reloc3r, a simple yet effective visual localization framework. It consists of an elegantly designed relative pose regression network, and a minimalist motion averaging module for absolute pose estimation. Trained on approximately eight million posed image pairs, Reloc3r achieves surprisingly good performance and generalization ability. We conduct extensive experiments on six public datasets, consistently demonstrating the effectiveness and efficiency of the proposed method. It provides high-quality camera pose estimates in real time and generalizes to novel scenes. Code: https://github.com/ffrivera0/reloc3r.

• The paper introduces a novel relative pose regression network that uses a symmetric Vision Transformer architecture to enhance localization accuracy.
• It decouples metric translation from pose estimation by integrating a lightweight motion averaging module for real-time, reliable performance.
• The framework demonstrates robust generalization across six diverse datasets and reveals spontaneous patch-level correspondence development.

An Expert Analysis of "Reloc3r: Large-Scale Training of Relative Camera Pose Regression for Generalizable, Fast, and Accurate Visual Localization"

The paper "Reloc3r: Large-Scale Training of Relative Camera Pose Regression for Generalizable, Fast, and Accurate Visual Localization" presents a new framework for visual localization that addresses several limitations of existing methods. The authors introduce a relative pose regression network termed Reloc3r, which, through extensive training on approximately 8 million image pairs, demonstrates enhanced capabilities in terms of generalization, speed, and accuracy in camera pose estimation. Here we provide an expert analysis of the key contributions and potential implications of this work for the field.

Innovations and Methodology

Reloc3r is designed to outperform prior visual localization techniques by tackling the prevailing challenges of scene generalization, inference efficiency, and pose accuracy. The framework consists of two primary components: a relative pose regression network supported by a motion averaging module for absolute pose determination.

The regression network borrows architectural insights from large-scale foundation models and employs a fully symmetric Vision Transformer (ViT) design. This innovative design differs from prior asymmetric models by sharing weights across branches, thereby simplifying the model and leveraging the inherent symmetry in relative pose estimation. Additionally, rather than regressing metric translations directly—an approach that can introduce challenges in dataset-generalization and learning efficacy—the model emphasizes learning the direction of camera translations. Notably, Reloc3r judiciously decouples metric scale determination from the pose regression process, instead utilizing a lightweight motion averaging strategy to estimate metric translation reliably.

Results and Performance Evaluation

The empirical evaluation of Reloc3r is comprehensive, covering six different datasets, including ScanNet1500, CO3Dv2, and Cambridge Landmarks. The framework displays competitive performance, often surpassing existing state-of-the-art methods in various benchmarks.

Specifically, the results demonstrate Reloc3r's superior pose accuracy as compared to other regression techniques and some feature-matching approaches across multiple test datasets. In pair-wise relative pose evaluations, Reloc3r consistently achieves high accuracy with significantly reduced inference times, notably achieving real-time processing capabilities. Furthermore, in the field of visual localization, Reloc3r outperforms existing methods on unseen datasets without scene-specific training, which underscores its robustness and versatility.

An interesting finding reported by the authors is the spontaneous development of patch-level correspondence capabilities within the trained network's features, suggesting potential pathways for further enhancements and insights into learned spatial relationships within transformer networks.

Implications and Future Directions

The methodological advancements presented in Reloc3r open new pathways for research in scalable localization and pose estimation. By leveraging large datasets and focusing on efficient architectural designs, this work illustrates the power and feasibility of comprehensive training strategies. The implications extend to fields reliant on accurate and rapid visual localization such as robotics, augmented reality, and autonomous navigation, where the ability to quickly process and adapt to new scenes is crucial.

Future research trajectories could involve extending the framework to incorporate contextual scene understanding or enhancing robustness against complex environmental dynamics. Additionally, integrating the model with intrinsic parameter learning might further refine its efficacy in diverse real-world scenarios.

In conclusion, Reloc3r presents a significant stride forward in the development of generalizable and efficient visual localization systems. Its purposeful combination of architectural simplicity and training scale stands as a testament to the potential of leveraging foundational model principles in advancing computer vision applications.