FUSELOC: Fusing Global and Local Descriptors to Disambiguate 2D-3D Matching in Visual Localization (2408.12037v1)

Abstract: Hierarchical methods represent state-of-the-art visual localization, optimizing search efficiency by using global descriptors to focus on relevant map regions. However, this state-of-the-art performance comes at the cost of substantial memory requirements, as all database images must be stored for feature matching. In contrast, direct 2D-3D matching algorithms require significantly less memory but suffer from lower accuracy due to the larger and more ambiguous search space. We address this ambiguity by fusing local and global descriptors using a weighted average operator within a 2D-3D search framework. This fusion rearranges the local descriptor space such that geographically nearby local descriptors are closer in the feature space according to the global descriptors. Therefore, the number of irrelevant competing descriptors decreases, specifically if they are geographically distant, thereby increasing the likelihood of correctly matching a query descriptor. We consistently improve the accuracy over local-only systems and achieve performance close to hierarchical methods while halving memory requirements. Extensive experiments using various state-of-the-art local and global descriptors across four different datasets demonstrate the effectiveness of our approach. For the first time, our approach enables direct matching algorithms to benefit from global descriptors while maintaining memory efficiency. The code for this paper will be published at \href{https://github.com/sontung/descriptor-disambiguation}{github.com/sontung/descriptor-disambiguation}.

• The paper introduces a novel fusion method that uses weighted averaging of global and local descriptors to disambiguate 2D-3D matching.
• The methodology reduces memory requirements by halving storage compared to hierarchical methods while enhancing localization precision.
• Experimental results across multiple datasets show a 7.7% reduction in median translation error, confirming its real-world efficacy.

FUSELOC: Fusing Global and Local Descriptors to Disambiguate 2D-3D Matching in Visual Localization

The paper presents "FUSELOC," a methodology aiming to improve visual localization techniques through the thoughtful fusion of global and local descriptors. The motivation behind this work stems from the limitations prevalent in both hierarchical visual localization and direct 2D-3D matching methods. Specifically, hierarchical methods require significant memory overhead to store global descriptors for image retrieval, while direct matching approaches, though memory-efficient, suffer from substantial inaccuracy due to ambiguous and large search spaces.

Methodology

FUSELOC proposes a hybrid approach that incorporates global descriptors to enhance the discriminability of local descriptors within the context of a 2D-3D search framework. This fusion is achieved using a weighted average operator that rearranges the local descriptor space, making geographically close descriptors more proximate in the feature space when guided by global descriptors. The proposed method aims to reduce irrelevant competing descriptors, particularly those geographically distant from the query descriptor, thereby improving the likelihood of accurate matches.

Key methodological points include:

• Descriptor Fusion: Local and global descriptors are combined using a weighted average to improve the distinctiveness of local features. This approach reduces the ambiguity in direct matching systems while maintaining the memory benefits.
• Codebook Construction: For each point in the database, a mean descriptor is computed from its appearance across multiple database images. The descriptors used include both local and the corresponding global descriptors.
• Query Matching: At query time, descriptors for query image keypoints are fused similarly and matched against the precomputed codebook using nearest-neighbor search.

Experimental Evaluation

The efficacy of FUSELOC is demonstrated through extensive experiments across several large-scale datasets: Cambridge Landmarks, Aachen Day-Night v1.1, RobotCar Seasons v2, and Extended CMU Seasons. The notable results include:

• Memory Usage: The proposed method halves the memory requirement compared to hierarchical methods while significantly improving accuracy over local-only systems.
• Accuracy Improvements: FUSELOC showcases improved accuracy in 2D-3D matching by reducing false matches and leveraging global descriptor discriminability. For instance, on the Cambridge Landmarks dataset, the average median translation error is reduced by approximately 7.7%, outperforming other state-of-the-art methods.
• Robustness to Descriptor Truncation: Various methods for truncating global descriptors were tested, establishing the robustness of the proposed fusion approach.

Implications and Future Directions

The implications of this research are multifold. Practically, FUSELOC's improvement in accuracy and reduced memory requirements make it highly suitable for deployment in memory-constrained environments, such as mobile robotics and augmented reality applications. Theoretical implications include demonstrating the benefit of integrating global descriptors into direct matching frameworks, potentially sparking further research into optimizing this integration for even higher efficiency and accuracy.

Future developments may involve:

• Disambiguation of Co-visible Points: Investigating new methods to resolve ambiguities among closely located, co-visible points could further enhance performance.
• Dynamic Descriptor Weighting: Adaptive methods to dynamically adjust the weighting between local and global descriptors based on the context or environment could optimize search performance.
• Real-time Implementation: Translating these improvements into real-time processing capabilities for on-device implementation represents an essential step for practical deployment.

Overall, FUSELOC represents a significant advance in addressing the trade-offs between memory usage and localization accuracy. Its innovative fusion approach sets a precedent for future work aiming to refine visual localization methods, fostering potential breakthroughs in both theoretical and practical domains.

Note: The full code for this paper is available at the provided GitHub repository.