Learning to Produce Semi-dense Correspondences for Visual Localization (2402.08359v2)

Abstract: This study addresses the challenge of performing visual localization in demanding conditions such as night-time scenarios, adverse weather, and seasonal changes. While many prior studies have focused on improving image-matching performance to facilitate reliable dense keypoint matching between images, existing methods often heavily rely on predefined feature points on a reconstructed 3D model. Consequently, they tend to overlook unobserved keypoints during the matching process. Therefore, dense keypoint matches are not fully exploited, leading to a notable reduction in accuracy, particularly in noisy scenes. To tackle this issue, we propose a novel localization method that extracts reliable semi-dense 2D-3D matching points based on dense keypoint matches. This approach involves regressing semi-dense 2D keypoints into 3D scene coordinates using a point inference network. The network utilizes both geometric and visual cues to effectively infer 3D coordinates for unobserved keypoints from the observed ones. The abundance of matching information significantly enhances the accuracy of camera pose estimation, even in scenarios involving noisy or sparse 3D models. Comprehensive evaluations demonstrate that the proposed method outperforms other methods in challenging scenes and achieves competitive results in large-scale visual localization benchmarks. The code will be available.

• The paper introduces a novel method that directly converts semi-dense 2D-2D matches into robust 2D-3D correspondences, enhancing pose estimation in challenging environments.
• It employs a Point Inference Network to leverage geometric and visual cues, accurately predicting 3D scene coordinates from detected 2D keypoints.
• It integrates a Confidence-based Point Aggregation module to reduce outliers, consistently outperforming existing methods in noisy and sparse conditions.

Enhancing Visual Localization in Challenging Conditions with DeViLoc

Introduction to DeViLoc

The quest for robust visual localization has led to the development of a variety of strategies aimed at accurately determining a camera's position and orientation within a scene. Among these, structure-based methods have shown promising outcomes but often grapple with noisy and sparse 3D point clouds which can significantly hinder their performance. Addressing these limitations, a novel framework, DeViLoc (semi-Dense Visual Localization), emerges as a potent solution for generating reliable semi-dense 2D-3D correspondences, even in challenging environments characterized by sparse or noisy 3D models.

Key Contributions of DeViLoc

DeViLoc introduces distinct innovations to the field of visual localization:

• Direct Conversion of Semi-dense Matches: Unlike traditional approaches that solely rely on sparse feature matches, DeViLoc efficiently predicts dense 2D-3D correspondences by directly converting semi-dense 2D-2D matches obtained via detector-free image matching.
• Point Inference Network (PIN): The framework employs a specialized network designed to transform detected 2D keypoints into corresponding 3D scene coordinates by leveraging both geometric and visual cues. This approach optimizes the use of available 3D information, enhancing the accuracy and reliability of inferred 3D points.
• Confidence-based Point Aggregation (CPA): To further refine the generated matches, DeViLoc incorporates a CPA module, which aggregates 2D-3D matches from multiple views based on confidence levels. This process effectively reduces outliers, leading to more precise camera pose estimations.

Performance and Evaluation

Thorough evaluations reveal that DeViLoc surpasses competing methods in various benchmarks, particularly in scenes fraught with challenging conditions such as night-time scenarios, adverse weather, and drastic seasonal changes. Its robust performance is attributed to its novel approach in handling noisy or sparse 3D inputs, a common limitation among current localization techniques.

Key findings from the conducted experiments include:

• Stable Performance Across Scenes: DeViLoc consistently achieves competitive results across a range of indoor and outdoor scenes, demonstrating its versatility and reliability.
• Superiority in Handling Noisy Inputs: The framework's resilience against noisy and sparse 3D models is a significant advancement, making it applicable in a broader spectrum of real-world scenarios.

Future Perspective and Limitations

While DeViLoc represents a significant step forward, the potential for enhancements remains. Currently, the runtime performance, particularly when handling a large number of reference images, poses a constraint on its scalability. Future work could explore optimizing the computational efficiency to expand its applicability further.

Moreover, adapting DeViLoc to more extensive datasets and varying conditions could provide deeper insights into its robustness and versatility. Integrating adaptive mechanisms to adjust confidence thresholds dynamically based on scene characteristics may also yield improvements in match filtering and pose estimation accuracy.

Conclusion

DeViLoc's introduction constitutes a substantial advancement in the field of visual localization, offering a compelling solution to some of the most pressing challenges faced by contemporary methods. Its ability to generate reliable semi-dense 2D-3D correspondences places it at the forefront of efforts to enhance localization accuracy in complex environments. As research progresses, DeViLoc's innovative approach could pave the way toward more resilient and versatile localization systems, unlocking new possibilities in robotics, augmented reality, and beyond.