EI-Nexus: Towards Unmediated and Flexible Inter-Modality Local Feature Extraction and Matching for Event-Image Data (2410.21743v1)

Abstract: Event cameras, with high temporal resolution and high dynamic range, have limited research on the inter-modality local feature extraction and matching of event-image data. We propose EI-Nexus, an unmediated and flexible framework that integrates two modality-specific keypoint extractors and a feature matcher. To achieve keypoint extraction across viewpoint and modality changes, we bring Local Feature Distillation (LFD), which transfers the viewpoint consistency from a well-learned image extractor to the event extractor, ensuring robust feature correspondence. Furthermore, with the help of Context Aggregation (CA), a remarkable enhancement is observed in feature matching. We further establish the first two inter-modality feature matching benchmarks, MVSEC-RPE and EC-RPE, to assess relative pose estimation on event-image data. Our approach outperforms traditional methods that rely on explicit modal transformation, offering more unmediated and adaptable feature extraction and matching, achieving better keypoint similarity and state-of-the-art results on the MVSEC-RPE and EC-RPE benchmarks. The source code and benchmarks will be made publicly available at https://github.com/ZhonghuaYi/EI-Nexus_official.

• The paper introduces a novel framework that directly extracts and matches features from event and image data without intermediary transformations.
• It employs Local Feature Distillation and Context Aggregation to bridge modality gaps using attention mechanisms and pre-trained image extractors.
• Empirical evaluations on MVSEC-RPE and EC-RPE benchmarks show improved keypoint similarity, reduced angular errors, and robust performance in asynchronous settings.

An Expert Overview of "EI-Nexus: Towards Unmediated and Flexible Inter-Modality Local Feature Extraction and Matching for Event-Image Data"

The paper "EI-Nexus: Towards Unmediated and Flexible Inter-Modality Local Feature Extraction and Matching for Event-Image Data" presents a novel framework explicitly tailored for the inter-modality local feature extraction and feature matching between event cameras and traditional image sensors. The research identifies and addresses a critical gap in the use of event cameras—devices that offer unique advantages such as high temporal resolution and dynamic contrast capabilities—in conjunction with conventional RGB imaging systems.

Core Contributions and Methodology

The proposed EI-Nexus framework optimizes feature extraction and matching across modalities without resorting to intermediary transformations that are commonly applied in conventional pipelines. This approach is encapsulated in a streamlined process that directly extracts keypoints from both the event data and the image data, subsequently utilizing a matching stage to establish correspondences.

The key innovative components of EI-Nexus include:

1. Local Feature Distillation (LFD): This component is crucial for effective local feature extraction. LFD bridges the modality gap by transferring viewpoint-invariant features from a pre-trained image extractor to the event data extractor, thereby aligning cross-modality feature spaces.
2. Context Aggregation (CA): To ensure robust feature matching, CA is employed within a learnable matching framework (such as LightGlue). This approach leverages attention mechanisms to aggregate contextual information, enhancing accuracy in matching across different modalities.
3. Flexible Framework: The modularity of EI-Nexus allows for various configurations of image extractors and matchers, demonstrating adaptability to advanced feature extraction and matching methodologies.

To rigorously assess the proposed framework, the authors established the MVSEC-RPE and EC-RPE benchmarks, the first of their kind to measure relative pose estimation on event-image data. This empirical evaluation demonstrated EI-Nexus's capacity to outperform traditional methods that rely on explicit modality transformations, offering improved keypoint similarity and state-of-the-art results against these novel benchmarks.

Empirical Evaluation

The experiments conducted across MVSEC and EC datasets underscored the superiority of EI-Nexus in achieving high repeatability, valid descriptor distances, and improved angular errors in pose estimation tasks. It effectively mitigated artifacts and inconsistencies often observed in methods requiring explicit event-to-video transformations. Furthermore, EI-Nexus demonstrated robustness across different event representations despite inherent limitations in modality discrepancies.

The framework displayed temporal robustness, effectively adapting across varying event time horizons. Ablative studies further validated the importance of balancing different loss functions within LFD to optimize local feature extraction. Additionally, the framework's performance was noted to be consistent across separate benchmarks, even when event data was paired with RGB images in asynchronous settings.

Implications and Future Directions

EI-Nexus sets a new precedent in the domain of multi-modal computer vision applications, particularly in tasks needing harmony between dynamic event data and frame-based image data, such as SLAM, object tracking, and visual localization. The direct extraction mechanism reduces computational overhead and embodies a versatile alternative to more complexity-laden interpolation strategies.

Looking ahead, potential expansions of this work could explore more sophisticated event data representation methods to enhance cross-modal feature alignment further. Additionally, given the evolution of hybrid sensors that couple event and frame data, this research could inform the design of next-generation devices and algorithms that natively integrate multi-modal sensing modalities. The EI-Nexus framework may also facilitate training across synthetic datasets, supporting broader generalization and robustness in diverse environmental conditions. The authors' open-source commitment to release their benchmark dataset and source code promises to encourage collaboration and foster future advancements in this emergent area of computer vision research.