# Long-Lived Accurate Keypoints in Event Streams

arXiv: https://arxiv.org/abs/2209.10385 · HTML: https://mlsanity.com/paper/2209.10385/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 22 references that are in the map.
- First posted on arXiv 21 Sep 2022.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [Unsupervised Event-based Learning of Optical Flow, Depth, and Egomotion](https://mlsanity.com/paper/1812.08156/) (2018)
- [The Event-Camera Dataset and Simulator: Event-based Data for Pose Estimation, Visual Odometry, and SLAM](https://mlsanity.com/paper/1610.08336/) (2016)
- [Events-to-Video: Bringing Modern Computer Vision to Event Cameras](https://mlsanity.com/paper/1904.08298/) (2019)
- [Speed Invariant Time Surface for Learning to Detect Corner Points with Event-Based Cameras](https://mlsanity.com/paper/1903.11332/) (2019)
- [v2e: From Video Frames to Realistic DVS Events](https://mlsanity.com/paper/2006.07722/) (2020)
- [LuvHarris: A Practical Corner Detector for Event-cameras](https://mlsanity.com/paper/2105.11443/) (2021)
- [Unraveling the paradox of intensity-dependent DVS pixel noise](https://mlsanity.com/paper/2109.08640/) (2021)

## Built on by (most cited first, 6 of 6)

- [Data-Driven Feature Tracking for Event Cameras With and Without Frames](https://mlsanity.com/paper/2211.12826/) (2022)
- [Tracking Any Point with Frame-Event Fusion Network at High Frame Rate](https://mlsanity.com/paper/2409.11953/) (2024)
- [Event Camera Meets Mobile Embodied Perception: Abstraction, Algorithm, Acceleration, Application](https://mlsanity.com/paper/2503.22943/) (2025)
- [SuperEvent: Cross-Modal Learning of Event-based Keypoint Detection for SLAM](https://mlsanity.com/paper/2504.00139/) (2025)
- [REALM: An RGB- and Event-Aligned Latent Manifold for Cross-Modal Perception](https://mlsanity.com/paper/2605.00271/) (2026)
- [E-S2Feat:Semantic-Guided Spiking Local Feature Detection and Description for Event Cameras](https://mlsanity.com/paper/2608.14027/) (2026)

## Abstract (arXiv)

We present a novel end-to-end approach to keypoint detection and tracking in an event stream that provides better precision and much longer keypoint tracks than previous methods. This is made possible by two contributions working together. First, we propose a simple procedure to generate stable keypoint labels, which we use to train a recurrent architecture. This training data results in detections that are very consistent over time. Moreover, we observe that previous methods for keypoint detection work on a representation (such as the time surface) that integrates events over a period of time. Since this integration is required, we claim it is better to predict the keypoints' trajectories for the time period rather than single locations, as done in previous approaches. We predict these trajectories in the form of a series of heatmaps for the integration time period. This improves the keypoint localization. Our architecture can also be kept very simple, which results in very fast inference times. We demonstrate our approach on the HVGA ATIS Corner dataset as well as "The Event-Camera Dataset and Simulator" dataset, and show it results in keypoint tracks that are three times longer and nearly twice as accurate as the best previous state-of-the-art methods. We believe our approach can be generalized to other event-based camera problems, and we release our source code to encourage other authors to explore it.