EPRBench: A High-Quality Benchmark Dataset for Event Stream Based Visual Place Recognition

2026-02-16 · Source: cs.NE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Data Science & Analytics · Depth: Expert, extended

Summary

Researchers introduce EPRBench, a high-quality benchmark dataset for event stream-based Visual Place Recognition (VPR), addressing the limitations of conventional cameras in challenging conditions like low light and high-speed motion. EPRBench contains 10,000 event sequences and 65,000 event frames, collected via handheld and vehicle-mounted setups across diverse viewpoints, weather, and lighting. It includes LLM-generated, human-refined scene descriptions to support semantic-aware and language-integrated VPR. The team also proposes SG-VPR, a novel multi-modal fusion paradigm that uses LLMs to generate textual scene descriptions from raw event streams, guiding spatially attentive token selection, cross-modal feature fusion, and multi-scale representation learning. SG-VPR achieves 94.3% R@1 on EPRBench and 57.5% R@1 on the NYC-Event-VPR dataset (Event modality), outperforming several state-of-the-art VPR algorithms and providing interpretable reasoning processes.

Key takeaway

For AI Scientists and Computer Vision Engineers developing autonomous systems, EPRBench offers a critical resource for advancing event-based VPR. Your models can now be trained and benchmarked against a large-scale, high-definition dataset with rich semantic annotations, enabling the development of more robust and interpretable localization solutions. Consider adopting the SG-VPR framework's multi-modal fusion and Chain-of-Thought reasoning to improve performance and transparency in challenging real-world deployments.

Key insights

Event cameras and LLMs enhance Visual Place Recognition robustness and interpretability in challenging environments.

Principles

• Event streams offer superior perception in extreme conditions.
• Semantic priors from LLMs improve VPR generalization and interpretability.
• Multi-modal fusion of visual and textual data enhances robustness.

Method

The SG-VPR framework uses DINOv2 for visual features and CLIP for text features, fusing them via global context aggregation and text-guided local sparsification, then applying multi-modal spatial pyramid aggregation.

In practice

• Utilize event cameras for VPR in low-light or high-speed scenarios.
• Integrate LLMs to generate semantic scene descriptions for VPR.
• Employ text-guided token selection to filter visual noise.

Topics

• Visual Place Recognition
• Event Cameras
• Benchmark Datasets
• Large Language Models
• Multi-modal Fusion

Code references

• Event-AHU/Neuromorphic_ReID

Best for: Computer Vision Engineer, AI Scientist, Research Scientist, AI Researcher, Machine Learning Engineer, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.NE updates on arXiv.org.