Ensemble-Based Event Camera Place Recognition Under Varying Illumination

• URL: http://arxiv.org/abs/2509.01968v1
• Date: Tue, 02 Sep 2025 05:17:07 GMT
• Title: Ensemble-Based Event Camera Place Recognition Under Varying Illumination
• Abstract summary: Event cameras provide a high dynamic range and low latency, offering greater robustness to rapid motion and challenging lighting conditions.<n>We introduce an ensemble-based approach to event camera place recognition that combines sequence-matched results from multiple event-to-frame reconstructions, VPR feature extractors, and temporal resolutions.<n>Unlike previous event-based ensemble methods, which only utilise temporal resolution, our broader fusion strategy delivers significantly improved robustness under varied lighting conditions.
• Score: 15.33833908429706
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Compared to conventional cameras, event cameras provide a high dynamic range and low latency, offering greater robustness to rapid motion and challenging lighting conditions. Although the potential of event cameras for visual place recognition (VPR) has been established, developing robust VPR frameworks under severe illumination changes remains an open research problem. In this paper, we introduce an ensemble-based approach to event camera place recognition that combines sequence-matched results from multiple event-to-frame reconstructions, VPR feature extractors, and temporal resolutions. Unlike previous event-based ensemble methods, which only utilise temporal resolution, our broader fusion strategy delivers significantly improved robustness under varied lighting conditions (e.g., afternoon, sunset, night), achieving a 57% relative improvement in Recall@1 across day-night transitions. We evaluate our approach on two long-term driving datasets (with 8 km per traverse) without metric subsampling, thereby preserving natural variations in speed and stop duration that influence event density. We also conduct a comprehensive analysis of key design choices, including binning strategies, polarity handling, reconstruction methods, and feature extractors, to identify the most critical components for robust performance. Additionally, we propose a modification to the standard sequence matching framework that enhances performance at longer sequence lengths. To facilitate future research, we will release our codebase and benchmarking framework.

Related papers

• EvLIR: Learning Illumination Residuals from Ordered Events for Low-Light Image Enhancement [13.751247283539177]
  EvLIR learns illumination residuals from ordered events for low-light image enhancement.<n>On SDE and SDSD indoor/outdoor benchmarks, EvLIR achieves the best result on eleven of twelve dataset-metric pairs.
  arXiv  Detail & Related papers  (2026-06-28T14:47:49Z)
• AdaFuse-Det: Adaptive Cross-Modal Fusion of Event Cameras for Robust Object Detection in Low-Light RGB Imagery [2.804640719143954]
  We present AdaFuse-Det, a dual-stream framework that fuses CLAHE-enhanced RGB frames with voxelized event tensors.<n>On the LLE-VOS benchmark, AdaFuse-Det achieves a Recall of $65.54%$, Precision of $53.85%$, and F1-Score of $59.12%$ under severe illumination degradation.
  arXiv  Detail & Related papers  (2026-05-23T18:01:48Z)
• InterFuserDVS: Event-Enhanced Sensor Fusion for Safe RL-Based Decision Making [0.0]
  We introduce a novel token-based fusion strategy that incorporates accumulated event frames into the transformer-based backbone of InterFuser.<n>We evaluate our approach on the Car Learning to Act (CARLA) Leaderboard benchmarks, demonstrating that the inclusion of DVS improves the robustness of the driving agent.
  arXiv  Detail & Related papers  (2026-05-05T23:24:45Z)
• Self-supervised Event-based Monocular Depth Estimation using Cross-modal Consistency [18.288912105820167]
  We propose a self-supervised event-based monocular depth estimation framework named EMoDepth. EMoDepth constrains the training process using the cross-modal consistency from intensity frames that are aligned with events in the pixel coordinate. In inference, only events are used for monocular depth prediction.
  arXiv  Detail & Related papers  (2024-01-14T07:16:52Z)
• EventAid: Benchmarking Event-aided Image/Video Enhancement Algorithms with Real-captured Hybrid Dataset [55.12137324648253]
  Event cameras are emerging imaging technology that offers advantages over conventional frame-based imaging sensors in dynamic range and sensing speed. This paper focuses on five event-aided image and video enhancement tasks.
  arXiv  Detail & Related papers  (2023-12-13T15:42:04Z)
• Event-based Continuous Color Video Decompression from Single Frames [36.4263932473053]
  We present ContinuityCam, a novel approach to generate a continuous video from a single static RGB image and an event camera stream.<n>Our approach combines continuous long-range motion modeling with a neural synthesis model, enabling frame prediction at arbitrary times within the events.
  arXiv  Detail & Related papers  (2023-11-30T18:59:23Z)
• Implicit Event-RGBD Neural SLAM [54.74363487009845]
  Implicit neural SLAM has achieved remarkable progress recently. Existing methods face significant challenges in non-ideal scenarios. We propose EN-SLAM, the first event-RGBD implicit neural SLAM framework.
  arXiv  Detail & Related papers  (2023-11-18T08:48:58Z)
• Revisiting Event-based Video Frame Interpolation [49.27404719898305]
  Dynamic vision sensors or event cameras provide rich complementary information for video frame. estimating optical flow from events is arguably more difficult than from RGB information. We propose a divide-and-conquer strategy in which event-based intermediate frame synthesis happens incrementally in multiple simplified stages.
  arXiv  Detail & Related papers  (2023-07-24T06:51:07Z)
• Progressively Optimized Local Radiance Fields for Robust View Synthesis [76.55036080270347]
  We present an algorithm for reconstructing the radiance field of a large-scale scene from a single casually captured video. For handling unknown poses, we jointly estimate the camera poses with radiance field in a progressive manner. For handling large unbounded scenes, we dynamically allocate new local radiance fields trained with frames within a temporal window.
  arXiv  Detail & Related papers  (2023-03-24T04:03:55Z)
• Globally-Optimal Event Camera Motion Estimation [30.79931004393174]
  Event cameras are bio-inspired sensors that perform well in HDR conditions and have high temporal resolution. Event cameras measure asynchronous pixel-level changes and return them in a highly discretised format.
  arXiv  Detail & Related papers  (2022-03-08T08:24:22Z)
• ESL: Event-based Structured Light [62.77144631509817]
  Event cameras are bio-inspired sensors providing significant advantages over standard cameras. We propose a novel structured-light system using an event camera to tackle the problem of accurate and high-speed depth sensing.
  arXiv  Detail & Related papers  (2021-11-30T15:47:39Z)
• An Asynchronous Kalman Filter for Hybrid Event Cameras [13.600773150848543]
  Event cameras are ideally suited to capture HDR visual information without blur. conventional image sensors measure absolute intensity of slowly changing scenes effectively but do poorly on high dynamic range or quickly changing scenes. We present an event-based video reconstruction pipeline for High Dynamic Range scenarios.
  arXiv  Detail & Related papers  (2020-12-10T11:24:07Z)
• Learning Monocular Dense Depth from Events [53.078665310545745]
  Event cameras produce brightness changes in the form of a stream of asynchronous events instead of intensity frames. Recent learning-based approaches have been applied to event-based data, such as monocular depth prediction. We propose a recurrent architecture to solve this task and show significant improvement over standard feed-forward methods.
  arXiv  Detail & Related papers  (2020-10-16T12:36:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.