# Continuous-time Intensity Estimation Using Event Cameras

arXiv: https://arxiv.org/abs/1811.00386 · HTML: https://mlsanity.com/paper/1811.00386/

## Key facts

- Cited by 68 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2018.

## Built on by (most cited first, 30 of 68)

- [Event-Based Fusion for Motion Deblurring with Cross-modal Attention](https://mlsanity.com/paper/2112.00167/) (2021)
- [N-ImageNet: Towards Robust, Fine-Grained Object Recognition with Event Cameras](https://mlsanity.com/paper/2112.01041/) (2021)
- [Event-aided Direct Sparse Odometry](https://mlsanity.com/paper/2204.07640/) (2022)
- [Combining Events and Frames using Recurrent Asynchronous Multimodal Networks for Monocular Depth Prediction](https://mlsanity.com/paper/2102.09320/) (2021)
- [Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarks](https://mlsanity.com/paper/2302.08890/) (2023)
- [E-NeRF: Neural Radiance Fields from a Moving Event Camera](https://mlsanity.com/paper/2208.11300/) (2022)
- [Event-based Video Reconstruction via Potential-assisted Spiking Neural Network](https://mlsanity.com/paper/2201.10943/) (2022)
- [Motion Deblurring with Real Events](https://mlsanity.com/paper/2109.13695/) (2021)
- [Event Camera Data Pre-training](https://mlsanity.com/paper/2301.01928/) (2023)
- [HyperE2VID: Improving Event-Based Video Reconstruction via Hypernetworks](https://mlsanity.com/paper/2305.06382/) (2023)
- [Formulating Event-based Image Reconstruction as a Linear Inverse Problem with Deep Regularization using Optical Flow](https://mlsanity.com/paper/2112.06242/) (2021)
- [Label-Free Event-based Object Recognition via Joint Learning with Image Reconstruction from Events](https://mlsanity.com/paper/2308.09383/) (2023)
- [Real-Time Optical Flow for Vehicular Perception with Low- and High-Resolution Event Cameras](https://mlsanity.com/paper/2112.10591/) (2021)
- [Are High-Resolution Event Cameras Really Needed?](https://mlsanity.com/paper/2203.14672/) (2022)
- [How Many Events do You Need? Event-based Visual Place Recognition Using Sparse But Varying Pixels](https://mlsanity.com/paper/2206.13673/) (2022)
- [Stereo Hybrid Event-Frame (SHEF) Cameras for 3D Perception](https://mlsanity.com/paper/2110.04988/) (2021)
- [EvDNeRF: Reconstructing Event Data with Dynamic Neural Radiance Fields](https://mlsanity.com/paper/2310.02437/) (2023)
- [3D Pose Estimation of Two Interacting Hands from a Monocular Event Camera](https://mlsanity.com/paper/2312.14157/) (2023)
- [Asynchronous Blob Tracker for Event Cameras](https://mlsanity.com/paper/2307.10593/) (2023)
- [EventHDR: from Event to High-Speed HDR Videos and Beyond](https://mlsanity.com/paper/2409.17029/) (2024)
- [Event-based Continuous Color Video Decompression from Single Frames](https://mlsanity.com/paper/2312.00113/) (2023)
- [A Linear Comb Filter for Event Flicker Removal](https://mlsanity.com/paper/2205.08090/) (2022)
- [Event-guided Multi-patch Network with Self-supervision for Non-uniform Motion Deblurring](https://mlsanity.com/paper/2302.07689/) (2023)
- [Event-based Background-Oriented Schlieren](https://mlsanity.com/paper/2311.00434/) (2023)
- [Learning to Super-Resolve Blurry Images with Events](https://mlsanity.com/paper/2302.13766/) (2023)
- [Unsupervised Joint Learning of Optical Flow and Intensity with Event Cameras](https://mlsanity.com/paper/2503.17262/) (2025)
- [Temporal-Mapping Photography for Event Cameras](https://mlsanity.com/paper/2403.06443/) (2024)
- [Learning to See Through with Events](https://mlsanity.com/paper/2212.02219/) (2022)
- [Noise2Image: Noise-Enabled Static Scene Recovery for Event Cameras](https://mlsanity.com/paper/2404.01298/) (2024)
- [Generalized Event Cameras](https://mlsanity.com/paper/2407.02683/) (2024)

## Abstract (arXiv)

Event cameras provide asynchronous, data-driven measurements of local temporal contrast over a large dynamic range with extremely high temporal resolution. Conventional cameras capture low-frequency reference intensity information. These two sensor modalities provide complementary information. We propose a computationally efficient, asynchronous filter that continuously fuses image frames and events into a single high-temporal-resolution, high-dynamic-range image state. In absence of conventional image frames, the filter can be run on events only. We present experimental results on high-speed, high-dynamic-range sequences, as well as on new ground truth datasets we generate to demonstrate the proposed algorithm outperforms existing state-of-the-art methods.