Event Camera Guided Visual Media Restoration & 3D Reconstruction: A Survey
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Abstract (arXiv)
Event camera sensors are bio-inspired sensors which asynchronously capture per-pixel brightness changes and output a stream of events encoding the polarity, location and time of these changes. These systems are witnessing rapid advancements as an emerging field, driven by their low latency, reduced power consumption, and ultra-high capture rates. This survey explores the evolution of fusing event-stream captured with traditional frame-based capture, highlighting how this synergy significantly benefits various video restoration and 3D reconstruction tasks. The paper systematically reviews major deep learning contributions to image/video enhancement and restoration, focusing on two dimensions: temporal enhancement (such as frame interpolation and motion deblurring) and spatial enhancement (including super-resolution, low-light and HDR enhancement, and artifact reduction). This paper also explores how the 3D reconstruction domain evolves with the advancement of event driven fusion. Diverse topics are covered, with in-depth discussions on recent works for improving visual quality under challenging conditions. Additionally, the survey compiles a comprehensive list of openly available datasets, enabling reproducible research and benchmarking. By consolidating recent progress and insights, this survey aims to inspire further research into leveraging event camera systems, especially in combination with deep learning, for advanced visual media restoration and enhancement.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 136 of its 244 references that are in the map. First posted on arXiv 12 Sep 2025.
Builds on
136 of 244 references.
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38 more
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50 most cited of 136 All 136 (JSON)
Built on by
Citing papers per year: 2025: 2; 2026: 1.
3 papers.
- 2025EvRainDrop: HyperGraph-guided Completion for Effective Frame and Event Stream Aggregationcited by 2
- 2025A Lightweight 3D-CNN for Event-Based Human Action Recognition with Privacy-Preserving Potentialcited by 1
- 2026EventEgoHands++: Event-based Egocentric 3D Hand Mesh Reconstruction with Real Datasetcited by 0
- 2026EventEgoHands++: Event-based Egocentric 3D Hand Mesh Reconstruction with Real Dataset15 Sep 2026
- 2025EvRainDrop: HyperGraph-guided Completion for Effective Frame and Event Stream Aggregation26 Nov 2025
- 2025A Lightweight 3D-CNN for Event-Based Human Action Recognition with Privacy-Preserving Potential5 Nov 2025