# EV-Catcher: High-Speed Object Catching Using Low-latency Event-based Neural Networks

arXiv: https://arxiv.org/abs/2304.07200 · HTML: https://mlsanity.com/paper/2304.07200/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 33 references that are in the map.
- First posted on arXiv 14 Apr 2023.

## Builds on (most cited first, 12 of 12)

- [EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras](https://mlsanity.com/paper/1802.06898/) (2018)
- [Asynchronous, Photometric Feature Tracking using Events and Frames](https://mlsanity.com/paper/1807.09713/) (2018)
- [Learning Monocular Dense Depth from Events](https://mlsanity.com/paper/2010.08350/) (2020)
- [Focus Is All You Need: Loss Functions For Event-based Vision](https://mlsanity.com/paper/1904.07235/) (2019)
- [TLIO: Tight Learned Inertial Odometry](https://mlsanity.com/paper/2007.01867/) (2020)
- [EVDodgeNet: Deep Dynamic Obstacle Dodging with Event Cameras](https://mlsanity.com/paper/1906.02919/) (2019)
- [Event-driven Vision and Control for UAVs on a Neuromorphic Chip](https://mlsanity.com/paper/2108.03694/) (2021)
- [Globally Optimal Contrast Maximisation for Event-based Motion Estimation](https://mlsanity.com/paper/2002.10686/) (2020)
- [Fast Motion Understanding with Spatiotemporal Neural Networks and Dynamic Vision Sensors](https://mlsanity.com/paper/2011.09427/) (2020)
- [EVReflex: Dense Time-to-Impact Prediction for Event-based Obstacle Avoidance](https://mlsanity.com/paper/2109.00405/) (2021)
- [Motion Equivariant Networks for Event Cameras with the Temporal Normalization Transform](https://mlsanity.com/paper/1902.06820/) (2019)
- [Exploiting Event Cameras for Spatio-Temporal Prediction of Fast-Changing Trajectories](https://mlsanity.com/paper/2001.01248/) (2020)

## Built on by (most cited first, 16 of 16)

- [EvDNeRF: Reconstructing Event Data with Dynamic Neural Radiance Fields](https://mlsanity.com/paper/2310.02437/) (2023)
- [ESVO2: Direct Visual-Inertial Odometry with Stereo Event Cameras](https://mlsanity.com/paper/2410.09374/) (2024)
- [Event-based Continuous Color Video Decompression from Single Frames](https://mlsanity.com/paper/2312.00113/) (2023)
- [Event-guided Low-light Video Semantic Segmentation](https://mlsanity.com/paper/2411.00639/) (2024)
- [Un-EVIMO: Unsupervised Event-Based Independent Motion Segmentation](https://mlsanity.com/paper/2312.00114/) (2023)
- [Egocentric Event-Based Vision for Ping Pong Ball Trajectory Prediction](https://mlsanity.com/paper/2506.07860/) (2025)
- [EA: An Event Autoencoder for High-Speed Vision Sensing](https://mlsanity.com/paper/2507.06459/) (2025)
- [evTransFER: A Transfer Learning Framework for Event-based Facial Expression Recognition](https://mlsanity.com/paper/2508.03609/) (2025)
- [Fast Feature Field (F3): A Predictive Representation of Events](https://mlsanity.com/paper/2509.25146/) (2025)
- [EBS-EKF: Accurate and High Frequency Event-based Star Tracking](https://mlsanity.com/paper/2503.20101/) (2025)
- [Match-Any-Events: Zero-Shot Motion-Robust Feature Matching Across Wide Baselines for Event Cameras](https://mlsanity.com/paper/2604.18744/) (2026)
- [1000 Rallies: An Event-Camera Dataset and Real-Time Learned Ball-State Estimation for Robotic Table Tennis](https://mlsanity.com/paper/2606.25620/) (2026)
- [Neuromorphic Object Detection: An In-Depth Study and Future Directions](https://mlsanity.com/paper/2607.23576/) (2026)
- [LiteEvent-AE: Lightweight Autoencoder for Event-Based Vision on Low-Latency Energy-Constrained Edge Devices](https://mlsanity.com/paper/2608.21764/) (2026)
- [Can Spiking Neural Networks play pinball? A neuromorphic motion detector for target tracking](https://mlsanity.com/paper/2609.24403/) (2026)
- [Moving6DPoSe: A Multimodal Database for Monocular 6D Pose Estimation and Segmentation of Moving Objects](https://mlsanity.com/paper/2609.26161/) (2026)

## Abstract (arXiv)

Event-based sensors have recently drawn increasing interest in robotic perception due to their lower latency, higher dynamic range, and lower bandwidth requirements compared to standard CMOS-based imagers. These properties make them ideal tools for real-time perception tasks in highly dynamic environments. In this work, we demonstrate an application where event cameras excel: accurately estimating the impact location of fast-moving objects. We introduce a lightweight event representation called Binary Event History Image (BEHI) to encode event data at low latency, as well as a learning-based approach that allows real-time inference of a confidence-enabled control signal to the robot. To validate our approach, we present an experimental catching system in which we catch fast-flying ping-pong balls. We show that the system is capable of achieving a success rate of 81% in catching balls targeted at different locations, with a velocity of up to 13 m/s even on compute-constrained embedded platforms such as the Nvidia Jetson NX.