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

Source: arXiv - 2602.12919v1

Overview

Event‑based cameras output asynchronous streams of brightness changes, enabling perception where traditional frame‑based cameras struggle (e.g., darkness, motion blur). This paper introduces EPRBench, the first large‑scale, high‑quality benchmark for Event‑Stream Visual Place Recognition (VPR), and demonstrates how large language models (LLMs) can be fused with event data to boost recognition accuracy and explainability.

Key Contributions

• EPRBench dataset: 10 K event sequences (≈65 K event frames) captured from handheld and vehicle‑mounted rigs across varied viewpoints, weather, and illumination.
• Semantic annotations: LLM‑generated scene descriptions refined by human annotators, enabling language‑guided VPR research.
• Comprehensive baseline: Implementation and evaluation of 15 state‑of‑the‑art VPR methods on the new benchmark, establishing clear performance reference points.
• Multi‑modal fusion paradigm: A novel pipeline that (1) extracts textual scene cues from raw event streams using LLMs, (2) uses those cues for spatially attentive token selection, (3) performs cross‑modal feature fusion, and (4) learns multi‑scale representations.
• Interpretability: The framework produces human‑readable reasoning traces (e.g., “recognizing a “wet asphalt road” under low light”), improving model transparency.
• Open‑source release: Dataset, code, and pretrained models are made publicly available on GitHub.

Methodology

1. Data acquisition – Event cameras (e.g., Prophesee Metavision) recorded continuous streams while a user walked or a vehicle drove through urban, suburban, and indoor environments. Each sequence was segmented into short “event frames” (fixed‑time windows) and paired with GPS‑based place labels.
2. Semantic labeling – Raw event streams were fed to a large language model (GPT‑4‑style) that generated concise scene descriptions (e.g., “tree‑lined street at dusk”). Human annotators then corrected any inaccuracies, producing a high‑quality text corpus aligned with each event frame.
3. Baseline VPR pipelines – Existing VPR algorithms (NetVLAD, DELG, SuperGlue, etc.) were adapted to consume event frames by converting them into pseudo‑images (event count maps) or spiking‑neural‑network embeddings.
4. Proposed fusion architecture
   • LLM encoder converts the textual description into a dense language embedding.
   • Event encoder (a spiking CNN or transformer) extracts spatiotemporal tokens from the event frame.
   • Spatial attention uses the language embedding to weight tokens that are semantically relevant (e.g., “road markings”).
   • Cross‑modal fusion merges weighted event tokens with language embeddings via a transformer‑style cross‑attention block.
   • Multi‑scale pooling aggregates features at several temporal resolutions, yielding a robust place descriptor.
5. Training & inference – The system is trained end‑to‑end with a contrastive loss that pulls together descriptors of the same place and pushes apart different places, while an auxiliary language‑guided loss encourages alignment between visual and textual semantics.

Results & Findings

• The LLM‑guided fusion outperforms all visual‑only baselines by ~15‑20 % absolute Recall@1, especially under extreme low‑light or high‑speed motion where event data alone is noisy.
• Ablation studies show that removing the language attention drops performance by ~7 %, confirming the complementary role of textual semantics.
• The reasoning output (e.g., highlighted tokens and generated description) aligns with human intuition in >90 % of cases, demonstrating effective interpretability.

Practical Implications

• Robotics & autonomous navigation – Vehicles operating at night, in tunnels, or during rapid maneuvers can rely on event‑based VPR for loop‑closure detection and map relocalization without expensive illumination hardware.
• AR/VR headsets – Low‑power event sensors combined with language‑aware place descriptors enable robust indoor localization when conventional cameras are blinded by motion or low light.
• Edge deployment – The pipeline runs on modest GPUs (or neuromorphic processors) with sub‑30 ms latency, making it suitable for real‑time SLAM on drones or handheld devices.
• Explainable AI – The textual reasoning can be surfaced to operators (e.g., “recognizing a “wet parking lot with orange cones””) to debug failures or certify safety‑critical systems.
• Cross‑modal research – The benchmark and code provide a testbed for future work on fusing event streams with other modalities (audio, LiDAR, radar) and with large‑scale language models.

Limitations & Future Work

• Dataset bias – EPRBench focuses on urban/suburban scenes; rural or highly dynamic environments (crowds, foliage) remain under‑represented.
• LLM dependency – The quality of textual cues hinges on the LLM’s prompting and may introduce hallucinations; tighter integration with domain‑specific vocabularies is needed.
• Hardware constraints – While inference is fast on GPUs, true low‑power deployment on dedicated neuromorphic chips still requires optimization.
• Future directions proposed by the authors include expanding the benchmark to multi‑sensor rigs (event + LiDAR), exploring self‑supervised language grounding to reduce annotation effort, and investigating continual‑learning schemes for long‑term place adaptation.

Authors

• Xiao Wang
• Xingxing Xiong
• Jinfeng Gao
• Xufeng Lou
• Bo Jiang
• Si-bao Chen
• Yaowei Wang
• Yonghong Tian

Paper Information

• arXiv ID: 2602.12919v1
• Categories: cs.CV, cs.AI, cs.NE
• Published: February 13, 2026
• PDF: Download PDF