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Event-Based Vision and Multimodal Sensor Fusion
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Event-Based Vision and Multimodal Sensor Fusion

A Special Issue of Sensors (ISSN 1424-8220) belonging to the section "Sensing and Imaging".

Deadline for manuscript submissions: 20 May 2027 | Viewed by 1483

Editors

Dr. Yi Chang

E-Mail Website
Guest Editor
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: image enhancement; computer vision
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Luxin Yan

E-Mail Website
Guest Editor
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: multispectral imaging processing; object detection
Special Issues, Collections and Topics in MDPI journals
Dr. Haoyue Liu

E-Mail Website
Guest Editor Assistant
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: event camera; high-speed imaging; HDR imaging; multimodal learning

Special Issue Information

Dear Colleagues,

Event-based vision has recently emerged as a disruptive sensing paradigm, inspired by the neuromorphic principles of the human retina. Unlike conventional frame-based cameras, event-based sensors asynchronously capture intensity changes with microsecond precision and a wide dynamic range, making them well-suited for fast motion, low-light, and high dynamic range environments. In parallel, multimodal sensor fusion has become an essential strategy to overcome the limitations of single modalities, enabling robust and comprehensive perception by integrating complementary information from diverse sensors, such as RGB cameras, LiDAR, radar, infrared, and inertial units.

This Special Issue aims to provide a platform for the latest developments in event-based vision and multimodal sensor fusion, covering both theoretical foundations and practical applications. It will highlight cutting-edge research on algorithms, architectures, and systems that advance the state-of-the-art in intelligent sensing and perception.

We welcome contributions that span a broad range of topics, including, but not limited to, event-based imaging and reconstruction, fusion frameworks, learning paradigms for cross-modal perception, and real-world applications in autonomous driving, robotics, augmented reality, biomedical imaging, and remote sensing. Both original research articles and comprehensive reviews are encouraged.

Dr. Yi Chang
Prof. Dr. Luxin Yan
Guest Editors

Dr. Haoyue Liu
Guest Editor Assistant

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • event-based vision
  • multimodal sensor fusion
  • cross-modal learning
  • autonomous systems and robotics
  • intelligent perception systems

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Published Papers (2 papers)

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Research

20 pages, 4904 KB  
Article
A Neuro-Inspired Rate-Encoded Descriptor for High-Speed Asynchronous Robotic Vision
by Shane Harrigan, Sonya Coleman, Dermot Kerr, Pratheepan Yogarajah, Chengdong Wu and Zheng Fang
Sensors 2026, 26(16), 5311; https://doi.org/10.3390/s26165311 - 21 Aug 2026
Viewed by 289
Abstract
This paper presents the Post-Stimulus Time-Dependent Event Descriptor (P-TED), a novel “pure event” feature descriptor designed for neuromorphic vision data. Unlike conventional frame-based approaches or hybrid methods that transform event data into intermediate representations, P-TED operates directly on asynchronous event streams, thereby preserving [...] Read more.
This paper presents the Post-Stimulus Time-Dependent Event Descriptor (P-TED), a novel “pure event” feature descriptor designed for neuromorphic vision data. Unlike conventional frame-based approaches or hybrid methods that transform event data into intermediate representations, P-TED operates directly on asynchronous event streams, thereby preserving the intrinsic low-latency and high-temporal-resolution advantages of event-based sensors. The descriptor integrates two complementary feature sets: a motion feature vector, which aggregates spatial relationships within a Moore neighbourhood to quantify stimulus direction, and a pattern feature vector, which employs rate encoding to capture temporal excitation signatures. The efficacy of the P-TED framework is validated through three distinct experiments: object and character recognition (MNIST-DVS and CIFAR10-DVS), mobile robot movement analysis, and complex non-rigid robotic hand gesture recognition (RoShamBo). Experimental results demonstrate that the P-TED achieves a significant reduction in classification latency, requiring only 2.7 ms compared to the 10.3 ms recorded by the state-of-the-art Distribution-Aware Retinal Transform (DART) framework. Additionally, P-TED exhibits superior robustness in disambiguating symmetric and mirrored motions, as well as in maintaining stability under non-linear fluctuations in event density caused by changing scale. This work establishes P-TED as a high-speed, computationally efficient, and explainable solution for real-time neuromorphic robotic vision systems. Full article
(This article belongs to the Special Issue Event-Based Vision and Multimodal Sensor Fusion)
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20 pages, 5197 KB  
Article
HDR Scene Reconstruction from Resolution-Mismatched Event Streams and Single-Exposure Images
by Zehao Chen, Binbin Zhou and Zengwei Zheng
Sensors 2026, 26(14), 4621; https://doi.org/10.3390/s26144621 - 21 Jul 2026
Viewed by 486
Abstract
High dynamic range (HDR) radiance-field reconstruction from single-exposure low dynamic range (LDR) images is limited by the information loss in saturated regions, while event cameras provide complementary measurements whose dynamic range far exceeds that of conventional sensors. In practical hybrid sensor systems, however, [...] Read more.
High dynamic range (HDR) radiance-field reconstruction from single-exposure low dynamic range (LDR) images is limited by the information loss in saturated regions, while event cameras provide complementary measurements whose dynamic range far exceeds that of conventional sensors. In practical hybrid sensor systems, however, the RGB camera usually has a higher spatial resolution than the event sensor, which makes existing event-aided HDR reconstruction methods difficult to apply directly. A straightforward solution is to super-resolve the event stream before reconstruction, but this 2D preprocessing introduces a global color cast and view-inconsistent high-frequency artifacts once the super-resolved events supervise a 3D radiance field. We propose a framework for HDR radiance-field reconstruction from resolution-mismatched event–image inputs. The framework incorporates a pretrained 2D event super-resolution prior and corrects its transfer to 3D reconstruction through a color correction module, which anchors the rendered radiance to the chrominance of the LDR images, and a dual-resolution event-stream constraint, which supervises the synthesized events at both the super-resolved and the native resolution. Experiments on the EvHDR-NeRF benchmark show that the proposed method achieves higher six-scene mean HDR fidelity than the strongest baseline while reducing the global color cast and alleviating multi-view artifacts. Full article
(This article belongs to the Special Issue Event-Based Vision and Multimodal Sensor Fusion)
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