FAR: Flexible, Accurate and Robust 6DoF Relative Camera Pose Estimation (2403.03221v1)

Abstract: Estimating relative camera poses between images has been a central problem in computer vision. Methods that find correspondences and solve for the fundamental matrix offer high precision in most cases. Conversely, methods predicting pose directly using neural networks are more robust to limited overlap and can infer absolute translation scale, but at the expense of reduced precision. We show how to combine the best of both methods; our approach yields results that are both precise and robust, while also accurately inferring translation scales. At the heart of our model lies a Transformer that (1) learns to balance between solved and learned pose estimations, and (2) provides a prior to guide a solver. A comprehensive analysis supports our design choices and demonstrates that our method adapts flexibly to various feature extractors and correspondence estimators, showing state-of-the-art performance in 6DoF pose estimation on Matterport3D, InteriorNet, StreetLearn, and Map-free Relocalization.

Overview of "FAR: Flexible, Accurate and Robust 6DoF Relative Camera Pose Estimation"

The paper "FAR: Flexible, Accurate and Robust 6DoF Relative Camera Pose Estimation" introduces an innovative approach in the field of computer vision, particularly focusing on the estimation of relative camera poses between images. This task is of critical importance in applications such as augmented reality, robotics, and autonomous driving. The paper seeks to enhance the precision and robustness of camera pose estimations through a novel integration of classical and learning-based methods.

Summary of the Approach

The authors address the trade-off between accuracy and robustness that is prevalent in existing methods. Traditional methods that rely on correspondences and solve for the fundamental matrix are noted for their high precision but struggle with large view changes and lack the ability to compute translation scale. On the other hand, deep learning approaches directly predicting pose are mentioned to be more robust and capable of gauging translation scale, though often less precise.

FAR achieves a balance by employing a Transformer-based architecture that synergistically utilizes both correspondence predictions and a solver for pose estimation. The Transformer model in FAR takes dense feature inputs and learns to weigh between solved and learned pose outputs, subsequently guiding a solver. This dual-path approach enables the system to adapt across varied input scenarios, leveraging strengths from both correspondence-based and learning-based methods.

Experimental Findings

The empirical evaluations in the paper assert that FAR outperforms existing state-of-the-art methods in terms of both accuracy and robustness across multiple datasets including Matterport3D, InteriorNet, StreetLearn, and Map-free Relocalization. The paper provides quantitative evidence of its superior performance through metrics such as mean and median rotation and translation errors.

For instance, in the Matterport3D dataset, FAR achieves significant improvements over previous methods, marking reductions in both median and mean translation errors, while also achieving better rotation precision. Notably, FAR exhibits high efficacy in low correspondence settings where robustness to noise and outliers is critical.

Implications and Future Directions

The robust performance of FAR across various challenging datasets highlights its practical applicability in real-world scenarios that involve complex camera movements and environmental conditions. As FAR efficiently synergizes classical pose algorithms with contemporary Transformers, it opens avenues for further exploration into hybrid models that can leverage domain-specific knowledge while integrating advanced learning techniques.

The adaptability of FAR across different feature extractors and correspondence estimators sets a precedent for future research aimed at generalizing camera pose estimation models across diverse real-world situations. Further enhancements might involve exploring more sophisticated forms of priors or fully integrating FAR into a broader context of SLAM systems, potentially enhancing performance in even more dynamic environments.

In summary, through its innovative integration of classical and neural network-based concepts, the FAR framework offers a promising direction for advancing the accuracy and robustness of camera pose estimation, underscoring significant potential for impact in various computer vision applications. The flexibility, accuracy, and robustness of FAR position it as a versatile tool for addressing the challenges of 6DoF relative camera pose estimation in future developments.