Relative Pose Estimation through Affine Corrections of Monocular Depth Priors

Abstract: Monocular depth estimation (MDE) models have undergone significant advancements over recent years. Many MDE models aim to predict affine-invariant relative depth from monocular images, while recent developments in large-scale training and vision foundation models enable reasonable estimation of metric (absolute) depth. However, effectively leveraging these predictions for geometric vision tasks, in particular relative pose estimation, remains relatively under explored. While depths provide rich constraints for cross-view image alignment, the intrinsic noise and ambiguity from the monocular depth priors present practical challenges to improving upon classic keypoint-based solutions. In this paper, we develop three solvers for relative pose estimation that explicitly account for independent affine (scale and shift) ambiguities, covering both calibrated and uncalibrated conditions. We further propose a hybrid estimation pipeline that combines our proposed solvers with classic point-based solvers and epipolar constraints. We find that the affine correction modeling is beneficial to not only the relative depth priors but also, surprisingly, the "metric" ones. Results across multiple datasets demonstrate large improvements of our approach over classic keypoint-based baselines and PnP-based solutions, under both calibrated and uncalibrated setups. We also show that our method improves consistently with different feature matchers and MDE models, and can further benefit from very recent advances on both modules. Code is available at https://github.com/MarkYu98/madpose.

• The paper introduces an affine correction model that improves pose estimation by addressing scale and shift ambiguities in monocular depth predictions.
• It presents specialized depth-aware solvers, including calibrated, shared-focal, and two-focal variants to optimize performance under varied camera conditions.
• Empirical results demonstrate significant improvements in pose error metrics across diverse datasets, confirming the method's robustness and practical applicability.

An Analysis of "Relative Pose Estimation through Affine Corrections of Monocular Depth Priors"

The paper "Relative Pose Estimation through Affine Corrections of Monocular Depth Priors" introduces a novel approach to enhance the relative pose estimation using monocular depth estimation (MDE) models. The advancements in MDE have made significant strides, but challenges persist in applying these estimations effectively for geometric vision tasks such as relative pose estimation. This paper addresses these challenges by proposing specialized solvers that account for affine ambiguities in depth predictions, including both scale and shift transformations, thereby offering a robust solution for pose estimation under both calibrated and uncalibrated conditions.

Key Contributions

1. Affine Correction Modeling: The study presents an affine correction model that incorporates both scale and shift parameters for depth estimates. The research demonstrates that even models trained for metric depth estimation benefit from this affine correction, leading to more consistent and accurate pose estimations.
2. Depth-Aware Solvers: The authors develop three solvers tailored for different conditions:
   • A calibrated solver that is minimal and tailored for known intrinsic scenarios using three points.
   • A shared-focal solver for situations where image pairs are captured with a common unknown focal length, requiring additional consideration for focal estimation.
   • A two-focal solver for circumstances with unknown, independent focal lengths.
3. Hybrid Estimation Pipeline: The solvers are integrated into a hybrid estimation pipeline combining classic point-based solvers with depth-aware solutions, optimizing both reprojection errors and traditional Sampson errors. This integration facilitates consistent improvements over classic approaches across various datasets.
4. Empirical Advancements: The empirical results presented across multiple datasets (ScanNet, MegaDepth, ETH3D) highlight substantial improvements in pose accuracy when integrating depth priors with the proposed affine corrections. This suggests the practical efficacy of combining new solvers with existing feature matchers and MDE models.

Numerical Results

The paper demonstrates strong numerical results showcasing considerable enhancements in pose error AUC and reduced median rotation and translation errors. Particularly noteworthy is the consistent improvement across both indoor and outdoor datasets, illustrating the robustness of the proposed method. Moreover, the hybrid pipeline's capability to integrate recent advancements in image matching and MDE indicates its adaptability and potential for future improvements.

Theoretical and Practical Implications

Theoretically, the paper extends the understanding of monocular depth estimation's role in enhancing geometric vision tasks by illustrating how affine invariants in depth can be utilized for precise pose estimation. Practically, the paper's methodology promises robust and reliable solutions in computer vision applications such as robotics, augmented reality, and autonomous systems, where accurate pose estimation is critical.

Future Directions

Future research could explore more sophisticated affine models that incorporate additional parameters or leverage machine learning techniques to predict these parameters directly. Furthermore, given the pipeline's adaptability, there is potential to include more advanced scenes and scenarios, especially leveraging datasets displaying high complexity or variability in depth perception.

In conclusion, this paper contributes significantly to the field of computer vision by bridging the gap between monocular depth estimation and pose estimation through innovative affine modeling techniques, presenting a valuable tool for researchers and practitioners aiming to enhance the accuracy and reliability of relative pose estimation tasks.