RePoseD: Efficient Relative Pose Estimation With Known Depth Information

Abstract: Recent advances in monocular depth estimation methods (MDE) and their improved accuracy open new possibilities for their applications. In this paper, we investigate how monocular depth estimates can be used for relative pose estimation. In particular, we are interested in answering the question whether using MDEs improves results over traditional point-based methods. We propose a novel framework for estimating the relative pose of two cameras from point correspondences with associated monocular depths. Since depth predictions are typically defined up to an unknown scale or even both unknown scale and shift parameters, our solvers jointly estimate the scale or both the scale and shift parameters along with the relative pose. We derive efficient solvers considering different types of depths for three camera configurations: (1) two calibrated cameras, (2) two cameras with an unknown shared focal length, and (3) two cameras with unknown different focal lengths. Our new solvers outperform state-of-the-art depth-aware solvers in terms of speed and accuracy. In extensive real experiments on multiple datasets and with various MDEs, we discuss which depth-aware solvers are preferable in which situation. The code will be made publicly available.

• The paper presents a framework to jointly estimate scale, shift, and camera pose from monocular depth, supporting calibrated and uncalibrated setups.
• Using efficient solutions, the framework achieves robust, state-of-the-art results on synthetic and real datasets tested with various depth predictors.
• The framework enables integrating monocular depth into high-precision camera pose estimation across various setups and predictors, useful for robotics and AR/VR.

An Overview of "Fixing the Scale and Shift in Monocular Depth for Camera Pose Estimation"

In the paper titled "Fixing the Scale and Shift in Monocular Depth For Camera Pose Estimation," the authors address the challenge of estimating the relative pose between two cameras using monocular depth predictions. Monocular depth prediction has advanced, providing a rich source of information for computer vision applications, such as Structure-from-Motion (SfM) and visual localization. However, these predictions are often influenced by unknown scale and shift parameters, posing a limitation to their direct application in precise camera pose estimation.

Core Contributions and Methodology

The authors propose a comprehensive framework that jointly estimates the scale, shift parameters, and the camera pose. They provide efficient solutions for different camera configurations:

1. Calibrated Cameras: The framework derives solutions for scenarios where the intrinsic parameters of the cameras are known. Here, a minimal number of three 3D-3D point correspondences are required.
2. Uncalibrated Cameras with Known Common Focal Length: For cameras sharing the same unknown focal length, a method is developed which necessitates four 2D-2D correspondences, from which three correspondences have monocular depths in both images.
3. Uncalibrated Cameras with Different Unknown Focal Lengths: This approach extends to cameras with differing focal lengths, requiring at least three 3D-3D point correspondences and one 3D-2D point correspondence to solve this 11-degrees-of-freedom problem.

Each configuration considers the specific geometric constraints introduced by the monocular depths and their associated uncertainties. The paper provides Gr\"{o}bner basis solutions for these problems and, in many cases, linear polynomial methods to minimize computational complexity.

Experimental Validation

The proposed solutions are validated through extensive experimental evaluations on both synthetic and real-world datasets, incorporating results from 11 different monocular depth prediction algorithms. The solvers demonstrate robust performance, establishing state-of-the-art results compared to existing methods in scenarios such as the Phototourism and ETH3D datasets.

A significant advantage of the proposed approach is its general applicability across different camera setups and depth predictors, making it a versatile choice for camera pose estimation tasks. Particularly, the paper highlights the accuracy improvements achievable when monocular depth predictions include the scale-and-shift parameters in their estimation process.

Implications and Future Directions

The work opens new avenues for integrating monocular depth estimations in real-world applications that require high precision in camera pose estimation. Future research could explore hybrid models that leverage both traditional feature matching and monocular depth information, potentially enhancing robustness in dynamic and occluded environments. Moreover, investigating real-time implementations of the proposed solutions could greatly benefit autonomous systems requiring live feedback and decision-making capabilities.

Overall, this paper contributes significantly to the body of knowledge in computer vision by bridging the gap between theoretical advancements in monocular depth prediction and practical applications in camera pose estimation. As AI and computer vision systems continue to evolve, such integrative approaches will likely play a crucial role in enhancing the autonomy and accuracy of perception systems in robotics and AR/VR technologies.