# EventGeM: Global-to-Local Feature Matching for Event-Based Visual Place Recognition

arXiv: https://arxiv.org/abs/2603.05807 · HTML: https://mlsanity.com/paper/2603.05807/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 49 references that are in the map.
- First posted on arXiv 6 Mar 2026.

## Builds on (most cited first, 30 of 35)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Fine-tuning CNN Image Retrieval with No Human Annotation](https://mlsanity.com/paper/1711.02512/) (2017)
- [Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition](https://mlsanity.com/paper/2103.01486/) (2021)
- [EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras](https://mlsanity.com/paper/1802.06898/) (2018)
- [Events-to-Video: Bringing Modern Computer Vision to Event Cameras](https://mlsanity.com/paper/1904.08298/) (2019)
- [Learning with Average Precision: Training Image Retrieval with a Listwise Loss](https://mlsanity.com/paper/1906.07589/) (2019)
- [Rethinking Visual Geo-localization for Large-Scale Applications](https://mlsanity.com/paper/2204.02287/) (2022)
- [AnyLoc: Towards Universal Visual Place Recognition](https://mlsanity.com/paper/2308.00688/) (2023)
- [Particular object retrieval with integral max-pooling of CNN activations](https://mlsanity.com/paper/1511.05879/) (2015)
- [MixVPR: Feature Mixing for Visual Place Recognition](https://mlsanity.com/paper/2303.02190/) (2023)
- [Optimal Transport Aggregation for Visual Place Recognition](https://mlsanity.com/paper/2311.15937/) (2023)
- [Asynchronous, Photometric Feature Tracking using Events and Frames](https://mlsanity.com/paper/1807.09713/) (2018)
- [EigenPlaces: Training Viewpoint Robust Models for Visual Place Recognition](https://mlsanity.com/paper/2308.10832/) (2023)
- [R2Former: Unified Retrieval and Reranking Transformer for Place Recognition](https://mlsanity.com/paper/2304.03410/) (2023)
- [CricaVPR: Cross-image Correlation-aware Representation Learning for Visual Place Recognition](https://mlsanity.com/paper/2402.19231/) (2024)
- [MegaLoc: One Retrieval to Place Them All](https://mlsanity.com/paper/2502.17237/) (2025)
- [Event Camera Data Pre-training](https://mlsanity.com/paper/2301.01928/) (2023)
- [Speed Invariant Time Surface for Learning to Detect Corner Points with Event-Based Cameras](https://mlsanity.com/paper/1903.11332/) (2019)
- [Event-based visual place recognition with ensembles of temporal windows](https://mlsanity.com/paper/2006.02826/) (2020)
- [Close, But Not There: Boosting Geographic Distance Sensitivity in Visual Place Recognition](https://mlsanity.com/paper/2407.02422/) (2024)
- [Event Camera Data Dense Pre-training](https://mlsanity.com/paper/2311.11533/) (2023)
- [How Many Events do You Need? Event-based Visual Place Recognition Using Sparse But Varying Pixels](https://mlsanity.com/paper/2206.13673/) (2022)
- [Dataset and Benchmark: Novel Sensors for Autonomous Vehicle Perception](https://mlsanity.com/paper/2401.13853/) (2024)
- [Depth AnyEvent: A Cross-Modal Distillation Paradigm for Event-Based Monocular Depth Estimation](https://mlsanity.com/paper/2509.15224/) (2025)
- [Enhancing Visual Place Recognition via Fast and Slow Adaptive Biasing in Event Cameras](https://mlsanity.com/paper/2403.16425/) (2024)
- [A compact neuromorphic system for ultra-energy-efficient, on-device robot localization](https://mlsanity.com/paper/2408.16754/) (2024)
- [NYC-Event-VPR: A Large-Scale High-Resolution Event-Based Visual Place Recognition Dataset in Dense Urban Environments](https://mlsanity.com/paper/2410.21615/) (2024)

## Built on by (most cited first, 1 of 1)

- [Multi-viewpoint Geo-localization with Event Cameras](https://mlsanity.com/paper/2609.21219/) (2026)

## Abstract (arXiv)

Event cameras are rapidly rising in popularity for robotic and computer vision tasks because their sparse activation delivers energy-efficient, high-dynamic-range, and fast sensing. Event cameras have been used in robotic navigation and localization tasks where positioning must occur in real time with sufficient accuracy. However, current event-based localization methods suffer from poor spatial understanding and are not viewpoint tolerant. In this paper, we address the problem of viewpoint-robust place recognition directly from event streams. We present EventGeM, a global-to-local feature fusion pipeline for event-based visual place recognition that combines whole-image feature detection to shortlist top candidates for 2D homography-based re-ranking with random sample consensus (RANSAC). We also contribute a regional generalized mean pooling (GeM) layer that learns to return the most relevant spatial features using per-row exponents to pool event streams into a compact global descriptor, trained on the NYC-Event-VPR dataset. These contributions overcome shortfalls in currently available event-based localization methods that fail to recognize similar places with large changes in viewpoint. To evaluate viewpoint-robust localization, we contribute a new event-based dataset that includes repeated traverses with a severe lateral shift. EventGeM improves absolute Recall@1 by 7 to 43 percentage points over the strongest baseline in each experiment. We also deploy EventGeM on a robotic platform, demonstrating real-time performance of our hierarchical pipeline. The code for EventGeM is available at https://github.com/AdamDHines/Event-GeM.