# EventPoint: Self-Supervised Interest Point Detection and Description for Event-based Camera

arXiv: https://arxiv.org/abs/2109.00210 · HTML: https://mlsanity.com/paper/2109.00210/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 40 references that are in the map.
- First posted on arXiv 1 Sep 2021.

## Builds on (most cited first, 17 of 17)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [Focal Loss for Dense Object Detection](https://mlsanity.com/paper/1708.02002/) (2017)
- [Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades](https://mlsanity.com/paper/1507.07629/) (2015)
- [LIFT: Learned Invariant Feature Transform](https://mlsanity.com/paper/1603.09114/) (2016)
- [DSEC: A Stereo Event Camera Dataset for Driving Scenarios](https://mlsanity.com/paper/2103.06011/) (2021)
- [Image Matching across Wide Baselines: From Paper to Practice](https://mlsanity.com/paper/2003.01587/) (2020)
- [R2D2: Repeatable and Reliable Detector and Descriptor](https://mlsanity.com/paper/1906.06195/) (2019)
- [E-RAFT: Dense Optical Flow from Event Cameras](https://mlsanity.com/paper/2108.10552/) (2021)
- [Universal Correspondence Network](https://mlsanity.com/paper/1606.03558/) (2016)
- [Speed Invariant Time Surface for Learning to Detect Corner Points with Event-Based Cameras](https://mlsanity.com/paper/1903.11332/) (2019)
- [DART: Distribution Aware Retinal Transform for Event-based Cameras](https://mlsanity.com/paper/1710.10800/) (2017)
- [Event Probability Mask (EPM) and Event Denoising Convolutional Neural Network (EDnCNN) for Neuromorphic Cameras](https://mlsanity.com/paper/2003.08282/) (2020)
- [UnsuperPoint: End-to-end Unsupervised Interest Point Detector and Descriptor](https://mlsanity.com/paper/1907.04011/) (2019)
- [High Speed Event Camera TRacking](https://mlsanity.com/paper/2010.02771/) (2020)
- [LuvHarris: A Practical Corner Detector for Event-cameras](https://mlsanity.com/paper/2105.11443/) (2021)

## Built on by (most cited first, 24 of 24)

- [Recent Event Camera Innovations: A Survey](https://mlsanity.com/paper/2408.13627/) (2024)
- [Depth AnyEvent: A Cross-Modal Distillation Paradigm for Event-Based Monocular Depth Estimation](https://mlsanity.com/paper/2509.15224/) (2025)
- [Data-efficient Event Camera Pre-training via Disentangled Masked Modeling](https://mlsanity.com/paper/2403.00416/) (2024)
- [Helios: An extremely low power event-based gesture recognition for always-on smart eyewear](https://mlsanity.com/paper/2407.05206/) (2024)
- [LiDAR-Event Stereo Fusion with Hallucinations](https://mlsanity.com/paper/2408.04633/) (2024)
- [Privacy-Preserving Visual Localization with Event Cameras](https://mlsanity.com/paper/2212.03177/) (2022)
- [SuperEvent: Cross-Modal Learning of Event-based Keypoint Detection for SLAM](https://mlsanity.com/paper/2504.00139/) (2025)
- [TESPEC: Temporally-Enhanced Self-Supervised Pretraining for Event Cameras](https://mlsanity.com/paper/2508.00913/) (2025)
- [Event Trojan: Asynchronous Event-based Backdoor Attacks](https://mlsanity.com/paper/2407.06838/) (2024)
- [LLM-EvRep: Learning an LLM-Compatible Event Representation Using a Self-Supervised Framework](https://mlsanity.com/paper/2502.14273/) (2025)
- [Hardware, Algorithms, and Applications of the Neuromorphic Vision Sensor: a Review](https://mlsanity.com/paper/2504.08588/) (2025)
- [evTransFER: A Transfer Learning Framework for Event-based Facial Expression Recognition](https://mlsanity.com/paper/2508.03609/) (2025)
- [Event Camera Guided Visual Media Restoration & 3D Reconstruction: A Survey](https://mlsanity.com/paper/2509.09971/) (2025)
- [Fast Feature Field (F3): A Predictive Representation of Events](https://mlsanity.com/paper/2509.25146/) (2025)
- [EI-Nexus: Towards Unmediated and Flexible Inter-Modality Local Feature Extraction and Matching for Event-Image Data](https://mlsanity.com/paper/2410.21743/) (2024)
- [TAPFormer: Robust Arbitrary Point Tracking via Transient Asynchronous Fusion of Frames and Events](https://mlsanity.com/paper/2603.04989/) (2026)
- [Match-Any-Events: Zero-Shot Motion-Robust Feature Matching Across Wide Baselines for Event Cameras](https://mlsanity.com/paper/2604.18744/) (2026)
- [EROAM: Event-based Camera Rotational Odometry and Mapping in Real-time](https://mlsanity.com/paper/2411.11004/) (2024)
- [SuperEIO: Self-Supervised Event Feature Learning for Event Inertial Odometry](https://mlsanity.com/paper/2503.22963/) (2025)
- [EventHub: Data Factory for Generalizable Event-Based Stereo Networks without Active Sensors](https://mlsanity.com/paper/2604.02331/) (2026)
- [REALM: An RGB- and Event-Aligned Latent Manifold for Cross-Modal Perception](https://mlsanity.com/paper/2605.00271/) (2026)
- [VGER: Voxel-Guided Global Event Ranking for Event Cloud Attribution](https://mlsanity.com/paper/2608.01470/) (2026)
- [Static in Frames, Dynamic in Events: Rethinking Features in Event Cameras as Motion Cues](https://mlsanity.com/paper/2608.11075/) (2026)
- [E-S2Feat:Semantic-Guided Spiking Local Feature Detection and Description for Event Cameras](https://mlsanity.com/paper/2608.14027/) (2026)

## Abstract (arXiv)

This paper proposes a self-supervised learned local detector and descriptor, called EventPoint, for event stream/camera tracking and registration. Event-based cameras have grown in popularity because of their biological inspiration and low power consumption. Despite this, applying local features directly to the event stream is difficult due to its peculiar data structure. We propose a new time-surface-like event stream representation method called Tencode. The event stream data processed by Tencode can obtain the pixel-level positioning of interest points while also simultaneously extracting descriptors through a neural network. Instead of using costly and unreliable manual annotation, our network leverages the prior knowledge of local feature extraction on color images and conducts self-supervised learning via homographic and spatio-temporal adaptation. To the best of our knowledge, our proposed method is the first research on event-based local features learning using a deep neural network. We provide comprehensive experiments of feature point detection and matching, and three public datasets are used for evaluation (i.e. DSEC, N-Caltech101, and HVGA ATIS Corner Dataset). The experimental findings demonstrate that our method outperforms SOTA in terms of feature point detection and description.