Abstract
Scene coordinate regression (SCR) methods are a family of visual localization methods that directly regress 2D-3D matches for camera pose estimation. They are effective in small-scale scenes but face significant challenges in large-scale scenes that are further amplified in the absence of ground truth 3D point clouds for supervision. Here, the model can only rely on reprojection constraints and needs to implicitly triangulate the points. The challenges stem from a fundamental dilemma: The network has to be invariant to observations of the same landmark at different viewpoints and lighting conditions, etc., but at the same time discriminate unrelated but similar observations. The latter becomes more relevant and severe in larger scenes. In this work, we tackle this problem by introducing the concept of co-visibility to the network. We propose GLACE, which integrates pre-trained global and local encodings and enables SCR to scale to large scenes with only a single small-sized network. Specifically, we propose a novel feature diffusion technique that implicitly groups the reprojection constraints with co-visibility and avoids overfitting to trivial solutions. Additionally, our position decoder parameterizes the output positions for large-scale scenes more effectively. Without using 3D models or depth maps for supervision, our method achieves state-of-the-art results on large-scale scenes with a low-map-size model. On Cambridge landmarks, with a single model, we achieve 17% lower median position error than Poker, the ensemble variant of the state-of-the-art SCR method ACE. Code is available at: https://github.com/cvg/glace.
Overview
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The paper introduces GLACE, a novel Scene Coordinate Regression (SCR) method that combines global and local encodings to manage large-scale visual localization without needing ground truth 3D point clouds.
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GLACE overcomes scalability challenges in SCR methods by using feature diffusion to implicitly group reprojection constraints, thus preventing overfitting and better handling varying viewpoints and lighting conditions.
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Empirical tests demonstrate GLACE's superior performance and efficiency in large-scale settings, with significant reductions in computational and storage requirements, making it suitable for practical applications like robotics, autonomous driving, and augmented reality.
GLACE: Global Local Accelerated Coordinate Encoding
The paper "GLACE: Global Local Accelerated Coordinate Encoding" explore the domain of Scene Coordinate Regression (SCR) methods for visual localization, specifically addressing their scalability challenges in large-scale scenes. This study, led by Fangjinhua Wang and colleagues, introduces GLACE, a novel approach that effectively integrates global and local encodings to enable SCR methods to manage large-scale environments with a single, compact model, without relying on ground truth 3D point clouds for supervision.
Visual localization involves estimating the camera position and orientation for a given query image within a pre-mapped environment. This functionality is critical in various applications including robotics, autonomous driving, and augmented reality. Traditional state-of-the-art localization methods typically rely on feature matching or scene coordinate regression. While feature matching methods store extensive point-wise visual descriptors for the entire point cloud, which is impractical for large scenes due to storage constraints, SCR methods encode the map information within a neural network, sidestepping the need for explicit descriptor matching.
Challenges in Large-Scale SCR
SCR methods, particularly ACE (Accelerated Coordinate Encoding), have shown state-of-the-art performance in small-scale scenes but face significant hurdles in scaling up. These challenges stem from the necessity for the network to be invariant to varying viewpoints and lighting conditions while simultaneously distinguishing between visually similar but unrelated scene observations. Such dilemmas become more pronounced in extensive scenes, where visually similar areas proliferate, increasing the ambiguity.
Proposed Method: GLACE
GLACE introduces the concept of co-visibility to the network by integrating pre-trained global and local encodings. This integration is achieved through a feature diffusion technique that implicitly groups reprojection constraints via co-visibility, thus preventing overfitting to trivial solutions. The key innovations in GLACE are:
- Global Encoding with Feature Diffusion: Adding Gaussian noise to the global features creates a distribution that enhances the likelihood of triangulating points observed from different viewpoints. This implicit grouping mechanism leverages the global feature's co-visibility information derived from an image retrieval model, ensuring that only co-visible images influence each other's coordinate estimation.
- Position Decoder: Unlike ACE, with a fixed center for training camera positions, GLACE uses a decoder that parameterizes the positional output as a convex combination of cluster centers. This approach mitigates the bias towards the center of training data and better generalizes to large-scale scenes by more effectively parameterizing the final positions.
Numerical Results and Implications
GLACE demonstrates its efficacy through extensive evaluations on multiple datasets, including 7/12 Scenes, integrated rooms (i12, i19), Cambridge Landmarks, and Aachen Day. Notable results include:
- On Cambridge Landmarks, GLACE achieves up to 17% lower median position error compared to the ensemble variant of ACE, Poker.
- For Aachen Day, GLACE outperforms an ensemble of 50 ACE models with significantly lesser map size and comparable accuracy metrics.
These empirical results underscore the robustness of GLACE in large-scale and diverse scene settings. The practical implications of this approach include reduced computational and storage requirements, enabling more efficient and practical deployments of SCR methods in real-world applications.
Theoretical and Practical Implications
Theoretically, GLACE expands the boundaries of SCR methods by facilitating effective handling of large-scale scenes with a single network. The incorporation of global encodings pre-trained on image retrieval tasks and the feature diffusion technique jointly address the intrinsic challenges of coordinate triangulation in extensive and complex environments. This methodological advancement propels the field towards more scalable and versatile SCR solutions.
Practically, the introduction of GLACE paves the way for deploying compact yet highly accurate visual localization models in scenarios where storage and computational efficiency are paramount. This makes it particularly beneficial for applications in robotics and augmented reality, where real-time processing and minimal resource usage are critical.
Future Directions
Future developments in this area could focus on enhancing the global encoding techniques further, perhaps exploring more sophisticated forms of feature augmentation or alternative pre-training tasks that could yield even more robust global features. Another avenue could involve refining the position decoder, allowing for finer control and better generalization across an even wider array of scene complexities and scales.
In conclusion, GLACE marks a significant step forward in the field of scene coordinate regression for visual localization, demonstrating that with innovative encoding and decoding strategies, it is possible to overcome the scalability issues inherent in large-scale scene reconstructions without the need for extensive ground truth annotations.
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