# Optimal biasing and physical limits of DVS event noise

arXiv: https://arxiv.org/abs/2304.04019 · HTML: https://mlsanity.com/paper/2304.04019/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Apr 2023.

## Built on by (most cited first, 14 of 14)

- [Enhancing Visual Place Recognition via Fast and Slow Adaptive Biasing in Event Cameras](https://mlsanity.com/paper/2403.16425/) (2024)
- [Deblur e-NeRF: NeRF from Motion-Blurred Events under High-speed or Low-light Conditions](https://mlsanity.com/paper/2409.17988/) (2024)
- [ADV2E: Bridging the Gap Between Analogue Circuit and Discrete Frames in the Video-to-Events Simulator](https://mlsanity.com/paper/2411.12250/) (2024)
- [Autobiasing Event Cameras](https://mlsanity.com/paper/2411.00729/) (2024)
- [Quantifying Noise of Dynamic Vision Sensor](https://mlsanity.com/paper/2404.01948/) (2024)
- [Optimal OnTheFly Feedback Control of Event Sensors](https://mlsanity.com/paper/2408.12976/) (2024)
- [On the Theory of Bias Tuning in Event Cameras](https://mlsanity.com/paper/2501.18788/) (2025)
- [BiasBench: A reproducible benchmark for tuning the biases of event cameras](https://mlsanity.com/paper/2504.18235/) (2025)
- [Learning to Robustly Reconstruct Low-light Dynamic Scenes from Spike Streams](https://mlsanity.com/paper/2401.10461/) (2024)
- [Characterization of dim light response in DVS pixel: Discontinuity of event triggering time](https://mlsanity.com/paper/2404.17771/) (2024)
- [Seeing the Unseen in Low-light Spike Streams](https://mlsanity.com/paper/2509.23304/) (2025)
- [Autobiasing Event Cameras for Flickering Mitigation](https://mlsanity.com/paper/2511.02180/) (2025)
- [Hybrid Event Frame Sensors: Modeling, Calibration, and Simulation](https://mlsanity.com/paper/2511.18037/) (2025)
- [Event Cameras for Melt-Pool Monitoring in Additive Manufacturing: A Benchmark and a Cross-Machine Transfer Analysis](https://mlsanity.com/paper/2610.06973/) (2026)

## Abstract (arXiv)

Under dim lighting conditions, the output of Dynamic Vision Sensor (DVS) event cameras is strongly affected by noise. Photon and electron shot-noise cause a high rate of non-informative events that reduce Signal to Noise ratio. DVS noise performance depends not only on the scene illumination, but also on the user-controllable biasing of the camera. In this paper, we explore the physical limits of DVS noise, showing that the DVS photoreceptor is limited to a theoretical minimum of 2x photon shot noise, and we discuss how biasing the DVS with high photoreceptor bias and adequate source-follower bias approaches optimal noise performance. We support our conclusions with pixel-level measurements of a DAVIS346 and analysis of a theoretical pixel model.