A Survey on Event-driven 3D Reconstruction: Development under Different Categories
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Abstract (arXiv)
Event cameras have gained increasing attention for 3D reconstruction due to their high temporal resolution, low latency, and high dynamic range. They capture per-pixel brightness changes asynchronously, allowing accurate reconstruction under fast motion and challenging lighting conditions. In this survey, we provide a comprehensive review of event-driven 3D reconstruction methods, including stereo, monocular, and multimodal systems. We further categorize recent developments based on geometric, learning-based, and hybrid approaches. Emerging trends, such as neural radiance fields and 3D Gaussian splatting with event data, are also covered. The related works are structured chronologically to illustrate the innovations and progression within the field. To support future research, we also highlight key research gaps and future research directions in dataset, experiment, evaluation, event representation, etc.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 39 of its 62 references that are in the map. First posted on arXiv 25 Mar 2025.
Builds on
39 of 62 references.
References · 39
- 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,226
- 2019Event-based Vision: A Surveycited by 1,016
- 2019High Speed and High Dynamic Range Video with an Event Cameracited by 345
- 2019Video to Events: Recycling Video Datasets for Event Camerascited by 165
- 2020Event-based Stereo Visual Odometrycited by 105
- 2022EventNeRF: Neural Radiance Fields from a Single Colour Event Cameracited by 92
- 2023Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarkscited by 90
- 2020Learning Monocular Dense Depth from Eventscited by 85
- 2022E-NeRF: Neural Radiance Fields from a Moving Event Cameracited by 70
- 2024Recent Event Camera Innovations: A Surveycited by 68
- 2018Semi-Dense 3D Reconstruction with a Stereo Event Cameracited by 67
- 2022Ev-NeRF: Event Based Neural Radiance Fieldcited by 59
27 more
- 2024EvaGaussians: Event Stream Assisted Gaussian Splatting from Blurry Imagescited by 56
- 2022DEVO: Depth-Event Camera Visual Odometry in Challenging Conditionscited by 36
- 2024Mitigating Motion Blur in Neural Radiance Fields with Events and Framescited by 31
- 2018Realtime Time Synchronized Event-based Stereocited by 28
- 2024E2GS: Event Enhanced Gaussian Splattingcited by 26
- 2024Event3DGS: Event-Based 3D Gaussian Splatting for High-Speed Robot Egomotioncited by 22
- 2024Beyond Gaussians: Fast and High-Fidelity 3D Splatting with Linear Kernelscited by 22
- 2022Multi-Event-Camera Depth Estimation and Outlier Rejection by Refocused Events Fusioncited by 21
- 2024IncEventGS: Pose-Free Gaussian Splatting from a Single Event Cameracited by 20
- 2025AE-NeRF: Augmenting Event-Based Neural Radiance Fields for Non-ideal Conditions and Larger Scenecited by 19
- 2024Ev-GS: Event-based Gaussian splatting for Efficient and Accurate Radiance Field Renderingcited by 18
- 20233D Pose Estimation of Two Interacting Hands from a Monocular Event Cameracited by 17
- 2020E3D: Event-Based 3D Shape Reconstructioncited by 13
- 2024EaDeblur-GS: Event assisted 3D Deblur Reconstruction with Gaussian Splattingcited by 12
- 2023EvAC3D: From Event-based Apparent Contours to 3D Models via Continuous Visual Hullscited by 11
- 2023E2PNet: Event to Point Cloud Registration with Spatio-Temporal Representation Learningcited by 11
- 2024Physical Priors Augmented Event-Based 3D Reconstructioncited by 11
- 2022Event-Based Dense Reconstruction Pipelinecited by 10
- 2024E2HQV: High-Quality Video Generation from Event Camera via Theory-Inspired Model-Aided Deep Learningcited by 10
- 2018Event-Based Structured Light for Depth Reconstruction using Frequency Tagged Light Patternscited by 9
- 2023EVI-SAM: Robust, Real-time, Tightly-coupled Event-Visual-Inertial State Estimation and 3D Dense Mappingcited by 9
- 2024Cross-Modal Semi-Dense 6-DoF Tracking of an Event Camera in Challenging Conditionscited by 9
- 2024EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Visioncited by 8
- 2023Dense Voxel 3D Reconstruction Using a Monocular Event Cameracited by 7
- 2025Towards End-to-End Neuromorphic Event-based 3D Object Reconstruction Without Physical Priorscited by 7
- 2024Event-based Structure-from-Orbitcited by 3
- 2025LLM-EvRep: Learning an LLM-Compatible Event Representation Using a Self-Supervised Frameworkcited by 3
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