# EV-IMO: Motion Segmentation Dataset and Learning Pipeline for Event Cameras

arXiv: https://arxiv.org/abs/1903.07520 · HTML: https://mlsanity.com/paper/1903.07520/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Mar 2019.

## Built on by (most cited first, 30 of 46)

- [Object Tracking by Jointly Exploiting Frame and Event Domain](https://mlsanity.com/paper/2109.09052/) (2021)
- [Recent Event Camera Innovations: A Survey](https://mlsanity.com/paper/2408.13627/) (2024)
- [Learning Dense and Continuous Optical Flow from an Event Camera](https://mlsanity.com/paper/2211.09078/) (2022)
- [EVIMO2: An Event Camera Dataset for Motion Segmentation, Optical Flow, Structure from Motion, and Visual Inertial Odometry in Indoor Scenes with Monocular or Stereo Algorithms](https://mlsanity.com/paper/2205.03467/) (2022)
- [SpikeMS: Deep Spiking Neural Network for Motion Segmentation](https://mlsanity.com/paper/2105.06562/) (2021)
- [Event3DGS: Event-Based 3D Gaussian Splatting for High-Speed Robot Egomotion](https://mlsanity.com/paper/2406.02972/) (2024)
- [Multi-Event-Camera Depth Estimation and Outlier Rejection by Refocused Events Fusion](https://mlsanity.com/paper/2207.10494/) (2022)
- [Event-based Stereo Depth Estimation: A Survey](https://mlsanity.com/paper/2409.17680/) (2024)
- [Stereo Hybrid Event-Frame (SHEF) Cameras for 3D Perception](https://mlsanity.com/paper/2110.04988/) (2021)
- [Neuromorphic Camera Denoising using Graph Neural Network-driven Transformers](https://mlsanity.com/paper/2112.09685/) (2021)
- [E2PNet: Event to Point Cloud Registration with Spatio-Temporal Representation Learning](https://mlsanity.com/paper/2311.18433/) (2023)
- [ProgressiveMotionSeg: Mutually Reinforced Framework for Event-Based Motion Segmentation](https://mlsanity.com/paper/2203.11732/) (2022)
- [Event-Free Moving Object Segmentation from Moving Ego Vehicle](https://mlsanity.com/paper/2305.00126/) (2023)
- [Un-EVIMO: Unsupervised Event-Based Independent Motion Segmentation](https://mlsanity.com/paper/2312.00114/) (2023)
- [Semantic-Aware Frame-Event Fusion based Pattern Recognition via Large Vision-Language Models](https://mlsanity.com/paper/2311.18592/) (2023)
- [Out of the Room: Generalizing Event-Based Dynamic Motion Segmentation for Complex Scenes](https://mlsanity.com/paper/2403.04562/) (2024)
- [NYC-Event-VPR: A Large-Scale High-Resolution Event-Based Visual Place Recognition Dataset in Dense Urban Environments](https://mlsanity.com/paper/2410.21615/) (2024)
- [Event-based Motion Segmentation by Cascaded Two-Level Multi-Model Fitting](https://mlsanity.com/paper/2111.03483/) (2021)
- [Object-centric Cross-modal Feature Distillation for Event-based Object Detection](https://mlsanity.com/paper/2311.05494/) (2023)
- [JSTR: Joint Spatio-Temporal Reasoning for Event-based Moving Object Detection](https://mlsanity.com/paper/2403.07436/) (2024)
- [Instance-Level Moving Object Segmentation from a Single Image with Events](https://mlsanity.com/paper/2502.12975/) (2025)
- [MTevent: A Multi-Task Event Camera Dataset for 6D Pose Estimation and Moving Object Detection](https://mlsanity.com/paper/2505.11282/) (2025)
- [The GRIFFIN Perception Dataset: Bridging the Gap Between Flapping-Wing Flight and Robotic Perception](https://mlsanity.com/paper/2101.10371/) (2021)
- [Wandering around: A bioinspired approach to visual attention through object motion sensitivity](https://mlsanity.com/paper/2502.06747/) (2025)
- [Bridge Frame and Event: Common Spatiotemporal Fusion for High-Dynamic Scene Optical Flow](https://mlsanity.com/paper/2503.06992/) (2025)
- [Tracking 6-DoF Object Motion from Events and Frames](https://mlsanity.com/paper/2103.15568/) (2021)
- [WorldGen: A Large Scale Generative Simulator](https://mlsanity.com/paper/2210.00715/) (2022)
- [Neuromorphic Vision-based Motion Segmentation with Graph Transformer Neural Network](https://mlsanity.com/paper/2404.10940/) (2024)
- [Finding Visual Saliency in Continuous Spike Stream](https://mlsanity.com/paper/2403.06233/) (2024)
- [TimeRewind: Rewinding Time with Image-and-Events Video Diffusion](https://mlsanity.com/paper/2403.13800/) (2024)

## Abstract (arXiv)

We present the first event-based learning approach for motion segmentation in indoor scenes and the first event-based dataset - EV-IMO - which includes accurate pixel-wise motion masks, egomotion and ground truth depth. Our approach is based on an efficient implementation of the SfM learning pipeline using a low parameter neural network architecture on event data. In addition to camera egomotion and a dense depth map, the network estimates pixel-wise independently moving object segmentation and computes per-object 3D translational velocities for moving objects. We also train a shallow network with just 40k parameters, which is able to compute depth and egomotion. Our EV-IMO dataset features 32 minutes of indoor recording with up to 3 fast moving objects simultaneously in the camera field of view. The objects and the camera are tracked by the VICON motion capture system. By 3D scanning the room and the objects, accurate depth map ground truth and pixel-wise object masks are obtained, which are reliable even in poor lighting conditions and during fast motion. We then train and evaluate our learning pipeline on EV-IMO and demonstrate that our approach far surpasses its rivals and is well suited for scene constrained robotics applications.