Map-free Visual Relocalization: Metric Pose Relative to a Single Image (2210.05494v1)

Abstract: Can we relocalize in a scene represented by a single reference image? Standard visual relocalization requires hundreds of images and scale calibration to build a scene-specific 3D map. In contrast, we propose Map-free Relocalization, i.e., using only one photo of a scene to enable instant, metric scaled relocalization. Existing datasets are not suitable to benchmark map-free relocalization, due to their focus on large scenes or their limited variability. Thus, we have constructed a new dataset of 655 small places of interest, such as sculptures, murals and fountains, collected worldwide. Each place comes with a reference image to serve as a relocalization anchor, and dozens of query images with known, metric camera poses. The dataset features changing conditions, stark viewpoint changes, high variability across places, and queries with low to no visual overlap with the reference image. We identify two viable families of existing methods to provide baseline results: relative pose regression, and feature matching combined with single-image depth prediction. While these methods show reasonable performance on some favorable scenes in our dataset, map-free relocalization proves to be a challenge that requires new, innovative solutions.

• The paper introduces a novel framework that estimates metric pose using only a single reference image.
• It compares relative pose regression and feature matching with depth prediction across 655 global locations to validate its approach.
• The study paves the way for scalable AR and robotics by reducing dependence on extensive 3D mapping.

Insights on Map-Free Visual Relocalization: Metric Pose Relative to a Single Image

The paper "Map-free Visual Relocalization: Metric Pose Relative to a Single Image" addresses the challenge of visual relocalization using a significantly reduced scene representation. Traditional methods typically depend on comprehensive 3D mapping that requires numerous reference images and subsequent scale calibrations. The proposed method intriguingly scales down the complexity by employing a single reference image, opening up novel avenues for applications in augmented reality (AR) and robotic navigation.

Core Contributions and Methods

The authors introduce "Map-free Relocalization," a task that leverages a single reference photograph for relocalizing queries in metric coordinates. This is in contrast to— and considerably more challenging than— standard methods requiring extensive datasets for constructing 3D maps. To support this new paradigm, the researchers have developed a specialized dataset comprising 655 diverse global locations, such as murals and sculptures, providing a single reference image for each.

To tackle the map-free relocalization task, the paper investigates two primary methodological approaches:

1. Relative Pose Regression (RPR) - This method involves training neural networks to estimate metric poses from a pair of images. The networks predict poses by learning from extensive training data across different environments.
2. Feature Matching with Depth Prediction - This involves matching features between the query and the reference image, augmented with depth predictions from deep single-image depth models to handle scale ambiguity.

The researchers identify that while existing methods show reasonable performance under favorable conditions, significant improvements are necessary for broad applicability. Feature matching coupled with depth prediction was found to outperform RPR in terms of metric accuracy and robustness, particularly when the systems are trained on datasets different from the test environments.

Results and Benchmarking

Performance evaluation is conducted on the custom dataset to benchmark the adaptability of both proposed methods. The results reflect a challenging scenario for state-of-the-art neural networks, with significant variations in scene conditions including lighting, viewpoints, and overlapping visual data. The paper establishes specific benchmarks such as rotation, translation errors, and a novel Virtual Correspondence Reprojection Error (VCRE) which gauges the misalignment of virtual AR content as a key measure of metric spatial accuracy.

In conventional datasets like 7Scenes, the map-free approach delivered competitive results, indicating the potential of these methods when fewer reference frames are available. Nonetheless, the inconsistency of depth prediction under diverse conditions highlights an opportunity for further refinement.

Practical and Theoretical Implications

From a practical standpoint, this paper addresses a critical bottleneck in scaling AR applications— the need for rapid scene understanding without prior extensive mapping. This could dramatically reduce preparation times for AR deployment, enhancing user experiences in dynamic environments.

Theoretically, this work expands the research horizon on relocalization by stressing the importance of monocular depth prediction accuracy and feature matching reliability in sparse data regimes. The new benchmark poses a compelling challenge for future research directions to improve these mechanisms, potentially through advancements in unsupervised or cross-domain depth training.

Conclusions and Future Directions

The proposed map-free relocalization framework, while not yet achieving fine-grained precision across all scenarios, makes a definitive statement about the feasibility of single-image-based relocalization. Future research might focus on improving depth prediction models for diverse outdoor datasets and devising more robust feature matching techniques that maintain high confidence levels in low-overlap conditions. The provision of a public dataset and benchmark by the authors is poised to serve as a pivotal resource in advancing the theoretical understanding and practical capabilities in this domain.