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[2602.12919] EPRBench: A High-Quality Benchmark Dataset for Event Stream Based Visual Place Recognition
Summary
EPRBench introduces a benchmark dataset for event stream-based visual place recognition, addressing challenges in low-light and high-speed conditions with 10K event sequences and advanced algorithm evaluations.
Why It Matters
This research is significant as it fills a gap in the availability of datasets for event stream-based visual place recognition, which is crucial for improving the robustness of computer vision systems in challenging environments. The integration of LLM-generated scene descriptions enhances the dataset's utility for semantic-aware applications, paving the way for advancements in AI and robotics.
Key Takeaways
- EPRBench provides a comprehensive dataset with 10K event sequences for visual place recognition.
- The dataset supports the evaluation of 15 state-of-the-art VPR algorithms, establishing a benchmark for future research.
- Integration of LLM-generated scene descriptions enhances the dataset's applicability for semantic tasks.
- A novel multi-modal fusion paradigm is proposed, improving model transparency and explainability.
- The dataset and source code will be publicly released, promoting further research in the field.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.12919 (cs) [Submitted on 13 Feb 2026] Title:EPRBench: A High-Quality Benchmark Dataset for Event Stream Based Visual Place Recognition Authors:Xiao Wang, Xingxing Xiong, Jinfeng Gao, Xufeng Lou, Bo Jiang, Si-bao Chen, Yaowei Wang, Yonghong Tian View a PDF of the paper titled EPRBench: A High-Quality Benchmark Dataset for Event Stream Based Visual Place Recognition, by Xiao Wang and 7 other authors View PDF HTML (experimental) Abstract:Event stream-based Visual Place Recognition (VPR) is an emerging research direction that offers a compelling solution to the instability of conventional visible-light cameras under challenging conditions such as low illumination, overexposure, and high-speed motion. Recognizing the current scarcity of dedicated datasets in this domain, we introduce EPRBench, a high-quality benchmark specifically designed for event stream-based VPR. EPRBench comprises 10K event sequences and 65K event frames, collected using both handheld and vehicle-mounted setups to comprehensively capture real-world challenges across diverse viewpoints, weather conditions, and lighting scenarios. To support semantic-aware and language-integrated VPR research, we provide LLM-generated scene descriptions, subsequently refined through human annotation, establishing a solid foundation for integrating LLMs into event-based perception pipelines. To facilitate systematic evaluation, we implement and benchmark 15...
