# SpikeCV: Open a Continuous Computer Vision Era

arXiv: https://arxiv.org/abs/2303.11684 · HTML: https://mlsanity.com/paper/2303.11684/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 68 references that are in the map.
- First posted on arXiv 21 Mar 2023.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Surrogate Gradient Learning in Spiking Neural Networks](https://mlsanity.com/paper/1901.09948/) (2019)
- [Video Enhancement with Task-Oriented Flow](https://mlsanity.com/paper/1711.09078/) (2017)
- [Noise2Noise: Learning Image Restoration without Clean Data](https://mlsanity.com/paper/1803.04189/) (2018)
- [Kornia: an Open Source Differentiable Computer Vision Library for PyTorch](https://mlsanity.com/paper/1910.02190/) (2019)
- [Learning Monocular Dense Depth from Events](https://mlsanity.com/paper/2010.08350/) (2020)
- [Event-Based Backpropagation can compute Exact Gradients for Spiking Neural Networks](https://mlsanity.com/paper/2009.08378/) (2020)
- [A Retina-inspired Sampling Method for Visual Texture Reconstruction](https://mlsanity.com/paper/1907.08769/) (2019)
- [Optical Flow Estimation for Spiking Camera](https://mlsanity.com/paper/2110.03916/) (2021)
- [1000x Faster Camera and Machine Vision with Ordinary Devices](https://mlsanity.com/paper/2201.09302/) (2022)
- [Strategy and Benchmark for Converting Deep Q-Networks to Event-Driven Spiking Neural Networks](https://mlsanity.com/paper/2009.14456/) (2020)
- [SlimYOLOv3: Narrower, Faster and Better for Real-Time UAV Applications](https://mlsanity.com/paper/1907.11093/) (2019)
- [Spike Camera and Its Coding Methods](https://mlsanity.com/paper/2104.04669/) (2021)
- [An Efficient Coding Method for Spike Camera using Inter-Spike Intervals](https://mlsanity.com/paper/1912.09669/) (2019)

## Built on by (most cited first, 7 of 7)

- [SpikeReveal: Unlocking Temporal Sequences from Real Blurry Inputs with Spike Streams](https://mlsanity.com/paper/2403.09486/) (2024)
- [SpikeMM: Flexi-Magnification of High-Speed Micro-Motions](https://mlsanity.com/paper/2406.00383/) (2024)
- [Rethinking High-speed Image Reconstruction Framework with Spike Camera](https://mlsanity.com/paper/2501.04477/) (2025)
- [SPKLIP: Aligning Spike Video Streams with Natural Language](https://mlsanity.com/paper/2505.12656/) (2025)
- [SpikeStereoNet: A Brain-Inspired Framework for Stereo Depth Estimation from Spike Streams](https://mlsanity.com/paper/2505.19487/) (2025)
- [SpikeGrasp: A Benchmark for 6-DoF Grasp Pose Detection from Stereo Spike Streams](https://mlsanity.com/paper/2510.10602/) (2025)
- [Current Injection Spiking Neural Network for Infrared and Visible Image Fusion](https://mlsanity.com/paper/2607.19879/) (2026)

## Abstract (arXiv)

SpikeCV is a new open-source computer vision platform for the spike camera, which is a neuromorphic visual sensor that has developed rapidly in recent years. In the spike camera, each pixel position directly accumulates the light intensity and asynchronously fires spikes. The output binary spikes can reach a frequency of 40,000 Hz. As a new type of visual expression, spike sequence has high spatiotemporal completeness and preserves the continuous visual information of the external world. Taking advantage of the low latency and high dynamic range of the spike camera, many spike-based algorithms have made significant progress, such as high-quality imaging and ultra-high-speed target detection. To build up a community ecology for the spike vision to facilitate more users to take advantage of the spike camera, SpikeCV provides a variety of ultra-high-speed scene datasets, hardware interfaces, and an easy-to-use modules library. SpikeCV focuses on encapsulation for spike data, standardization for dataset interfaces, modularization for vision tasks, and real-time applications for challenging scenes. With the advent of the open-source Python ecosystem, modules of SpikeCV can be used as a Python library to fulfilled most of the numerical analysis needs of researchers. We demonstrate the efficiency of the SpikeCV on offline inference and real-time applications. The project repository address are urlhttps://openi.pcl.ac.cn/Cordium/SpikeCV and urlhttps://github.com/Zyj061/SpikeCV