Implicit Learning of Scene Geometry from Poses for Global Localization
(2312.02029)Abstract
Global visual localization estimates the absolute pose of a camera using a single image, in a previously mapped area. Obtaining the pose from a single image enables many robotics and augmented/virtual reality applications. Inspired by latest advances in deep learning, many existing approaches directly learn and regress 6 DoF pose from an input image. However, these methods do not fully utilize the underlying scene geometry for pose regression. The challenge in monocular relocalization is the minimal availability of supervised training data, which is just the corresponding 6 DoF poses of the images. In this paper, we propose to utilize these minimal available labels (.i.e, poses) to learn the underlying 3D geometry of the scene and use the geometry to estimate the 6 DoF camera pose. We present a learning method that uses these pose labels and rigid alignment to learn two 3D geometric representations (\textit{X, Y, Z coordinates}) of the scene, one in camera coordinate frame and the other in global coordinate frame. Given a single image, it estimates these two 3D scene representations, which are then aligned to estimate a pose that matches the pose label. This formulation allows for the active inclusion of additional learning constraints to minimize 3D alignment errors between the two 3D scene representations, and 2D re-projection errors between the 3D global scene representation and 2D image pixels, resulting in improved localization accuracy. During inference, our model estimates the 3D scene geometry in camera and global frames and aligns them rigidly to obtain pose in real-time. We evaluate our work on three common visual localization datasets, conduct ablation studies, and show that our method exceeds state-of-the-art regression methods' pose accuracy on all datasets.
Overview
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The paper introduces a method that estimates the global pose of a camera from a single RGB image by learning 3D scene geometry in camera and global coordinate frames using 6 Degrees of Freedom (DoF) poses.
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The authors utilize a differentiable rigid alignment algorithm (weighted Kabsch algorithm) and introduce additional constraints via consistency and re-projection loss to align geometric representations with ground-truth poses, enhancing the accuracy of pose estimation.
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Extensive evaluations on various datasets show superior localization accuracy over state-of-the-art methods, and the approach is highly applicable for real-time applications with performance metrics up to 90 FPS.
Implicit Learning of Scene Geometry from Poses for Global Localization
This paper by Altillawi et al. addresses the challenge of global visual localization by proposing a novel approach that implicitly learns 3D scene geometry from minimal available labels — specifically, the 6 Degrees of Freedom (DoF) poses of images. This approach circumvents the necessity for explicit 3D geometric information or full maps, which are traditionally used in visual localization tasks.
Summary
The authors introduce a method that, given a single RGB image, estimates the global pose of the camera by learning the 3D scene geometry in two coordinate systems: the camera frame and the global frame. Unlike existing methods that treat pose estimation as a regression problem, this method leverages the underlying scene geometry for better accuracy.
The central idea hinges on the learning of two 3D scene representations from minimal supervision, i.e., the (X, Y, Z) coordinates in both camera and global coordinate frames, driven by the available pose labels. During training, these 3D coordinates are aligned using a rigid alignment algorithm, specifically the weighted Kabsch algorithm, to estimate the camera pose. The pose estimation is adjusted via gradient descent to match the ground-truth poses, thereby updating the 3D geometric representations.
Key Contributions
- Implicit Geometry Learning: The method assigns pose labels to train a deep neural network to learn geometric scene representations implicitly.
- Rigid Alignment: Utilization of a differentiable, parameter-free rigid alignment module allows the network to learn consistent geometric representations and compute poses in a closed-form solution.
- Additional Constraints: Introduction of a consistency loss aligns the two geometric representations with the ground-truth pose and a re-projection loss that aligns 3D global coordinates with 2D image pixels.
Experimental Results
The authors perform extensive evaluations on three datasets: Cambridge Landmarks, 7Scenes, and 12Scenes. The proposed method demonstrates superior performance, exceeding the localization accuracy of previous state-of-the-art regression methods.
- Cambridge Landmarks: The proposed method achieves an average localization error of 0.60 meters in translation and 1.62 degrees in rotation, showing significant improvement over previous methods.
- 7Scenes: The method yields an average localization error of 0.116 meters and 3.44 degrees, again outperforming state-of-the-art approaches.
- 12Scenes: With an error of 0.061 meters and 2.33 degrees, the method stands out in both translational and rotational accuracy.
Ablation Studies
Several ablation studies were conducted to observe the effects of different training losses and resolutions:
- Each additional loss (re-projection, consistency) contributed to improved localization accuracy.
- Learning depth instead of direct 3D coordinates in the camera frame yielded better results.
- MobileNetV3, as the backbone network, provided the best trade-off between run-time efficiency and localization accuracy.
Practical Implications
The authors demonstrate that their approach is highly applicable for real-time applications, reporting performance metrics showing FPS rates of up to 90 Hz. Furthermore, the method's ability to fine-tune with partial labels (position-only) opens possibilities for practical deployment in scenarios where full pose information may not be readily available.
Future Directions
The present study indicates potential for combining this method with foundational models for generating embeddings, which could be integrated into the learned geometric representations. Such advancements may elevate the accuracy of pose estimation through the incorporation of more contextual scene semantics.
In conclusion, this paper contributes a significant advancement in global visual localization by leveraging implicit learning of scene geometry from pose information, providing a framework that overcomes limitations of traditional regression-based methods and extending possibilities for efficient, accurate, real-time localization solutions.
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