Leveraging Semantic Cues from Foundation Vision Models for Enhanced Local Feature Correspondence (2410.09533v1)

Abstract: Visual correspondence is a crucial step in key computer vision tasks, including camera localization, image registration, and structure from motion. The most effective techniques for matching keypoints currently involve using learned sparse or dense matchers, which need pairs of images. These neural networks have a good general understanding of features from both images, but they often struggle to match points from different semantic areas. This paper presents a new method that uses semantic cues from foundation vision model features (like DINOv2) to enhance local feature matching by incorporating semantic reasoning into existing descriptors. Therefore, the learned descriptors do not require image pairs at inference time, allowing feature caching and fast matching using similarity search, unlike learned matchers. We present adapted versions of six existing descriptors, with an average increase in performance of 29% in camera localization, with comparable accuracy to existing matchers as LightGlue and LoFTR in two existing benchmarks. Both code and trained models are available at https://www.verlab.dcc.ufmg.br/descriptors/reasoning_accv24

• The paper introduces a novel method that integrates semantic cues from foundation vision models to enhance the accuracy of local feature descriptors.
• It employs an attention-based mechanism to combine semantic and texture features, achieving a 25% improvement in pose estimation on benchmarks such as Scannet1500 and 7Scenes.
• The approach enables efficient feature caching and matching, significantly benefiting large-scale applications like visual localization and Structure from Motion.

Leveraging Semantic Cues from Foundation Vision Models for Enhanced Local Feature Correspondence

This paper presents a method for improving local feature matching by integrating semantic cues from foundation vision models. The research addresses the limitations of existing learned sparse and dense matchers, which often struggle with matching points from different semantic areas and require image pairs for inference—resulting in inefficiency in large-scale applications like Structure from Motion (SfM).

Methodology and Contributions

The authors propose an innovative learning-based method that enhances local feature descriptors with semantic reasoning. By incorporating features from foundation models such as DINOv2, the method can leverage semantic underpinnings without relying on traditional two-view context aggregation techniques. This allows for feature caching and expedites similarity searches during matching, making it highly efficient and scalable.

The methodology integrates semantic context into local descriptors using an attention mechanism, refining descriptors through iterative cross-attention between semantic-based and texture-based features. The descriptors are then utilized with a mutual nearest neighbor search to identify visual correspondences between image pairs. This approach maintains the precision of texture-based descriptors while using semantic information to filter and enhance matches, resulting in more consistent and accurate correspondence.

Experimental Results

The experiments demonstrate significant enhancements in performance for indoor environments in tasks such as camera pose estimation and visual localization. Comparison with state-of-the-art models like LightGlue and LoFTR shows competitive results, indicating the effectiveness of semantic integration even when using single-view feature extraction.

1. Pose Estimation: Semantic conditioning improves baseline descriptors' performance, with an average improvement of 25% in pose estimation tasks, as shown in diverse benchmarks such as Scannet1500 and the 7Scenes dataset.
2. Visual Localization: The method shows superior accuracy in the 7Scenes benchmark, producing competitive error rates in comparison with current leading techniques.

These results substantiate the claim that integrating semantics can significantly bolster the robustness of feature matching in challenging visual conditions.

Implications and Future Work

The use of semantic cues offers a promising avenue for improving feature correspondence in computer vision, especially in scenarios with ambiguous or repetitive textures. This technique could open pathways for deploying similar strategies in various applications that require high-fidelity image matching, such as augmented reality or autonomous navigation systems.

The paper hints at broader implications for future AI developments, suggesting that incorporating semantic understanding in visual tasks could greatly enhance context-awareness and improve model generalization across different environments.

Conclusion

By leveraging semantic cues from foundation vision models, this work provides a substantial contribution to the field of computer vision, particularly in enhancing local feature correspondence. The methodology offers a balance between computational efficiency and matching accuracy, setting a precedent for further exploration of semantic integration in visual matching tasks. Future research could expand upon this foundation, exploring its applicability and performance improvements in diverse and more complex environments.