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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

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arxiv 2205.03467 v1 pith:J6RFI7OG submitted 2022-05-06 cs.CV cs.RO

Levi Burner , Anton Mitrokhin , Cornelia Fermüller , Yiannis Aloimonos This is my paper · ORCID
classification cs.CVcs.RO
keywords evimo2cameracameraseventmotionsequencesdatadataset
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abstract

A new event camera dataset, EVIMO2, is introduced that improves on the popular EVIMO dataset by providing more data, from better cameras, in more complex scenarios. As with its predecessor, EVIMO2 provides labels in the form of per-pixel ground truth depth and segmentation as well as camera and object poses. All sequences use data from physical cameras and many sequences feature multiple independently moving objects. Typically, such labeled data is unavailable in physical event camera datasets. Thus, EVIMO2 will serve as a challenging benchmark for existing algorithms and rich training set for the development of new algorithms. In particular, EVIMO2 is suited for supporting research in motion and object segmentation, optical flow, structure from motion, and visual (inertial) odometry in both monocular or stereo configurations. EVIMO2 consists of 41 minutes of data from three 640$\times$480 event cameras, one 2080$\times$1552 classical color camera, inertial measurements from two six axis inertial measurement units, and millimeter accurate object poses from a Vicon motion capture system. The dataset's 173 sequences are arranged into three categories. 3.75 minutes of independently moving household objects, 22.55 minutes of static scenes, and 14.85 minutes of basic motions in shallow scenes. Some sequences were recorded in low-light conditions where conventional cameras fail. Depth and segmentation are provided at 60 Hz for the event cameras and 30 Hz for the classical camera. The masks can be regenerated using open-source code up to rates as high as 200 Hz. This technical report briefly describes EVIMO2. The full documentation is available online. Videos of individual sequences can be sampled on the download page.

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Forward citations

Cited by 10 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. DSERT-RoLL: Robust Multi-Modal Perception for Diverse Driving Conditions with Stereo Event-RGB-Thermal Cameras, 4D Radar, and Dual-LiDAR

    cs.CV 2026-04 accept novelty 7.0 of 10

    A multi-sensor driving dataset (stereo event-RGB-thermal, 4D radar, dual LiDAR) under diverse weather and lighting, with 2D/3D benchmarks and a fusion method that improves 3D detection robustness.

  2. MTevent: A Multi-Task Event Camera Dataset for 6D Pose Estimation and Moving Object Detection

    cs.CV 2025-05 conditional novelty 7.0 of 10

    Introduces MTevent, an event camera dataset with 75 scenes, 16 objects, MoCap-based 6D pose and 3D box annotations, plus a FoundationPose RGB baseline with Average Recall 0.22.

  3. E2Pano: Learning Event-to-Panorama Image Reconstruction

    cs.CV 2026-08 conditional novelty 6.0 of 10

    E2Pano couples spherical event alignment with a learned Transformer-based photometric stage to reconstruct panoramas from event streams under rotational scanning, outperforming optimization baselines on a new syntheti...

  4. GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking

    cs.CV 2026-07 conditional novelty 6.0 of 10

    An RL agent that adaptively decides when to accumulate events and when to run tracking inference improves event-based feature tracking on a new dynamic benchmark, but the gains are less consistent on an existing benchmark.

  5. Motion Segmentation and Egomotion Estimation from Event-Based Normal Flow

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A pipeline that segments independently moving objects and estimates egomotion from event-based normal flow and IMU rotation, without optical flow or depth, validated on EVIMO2v2.

  6. Learning Normal Flow Directly From Event Neighborhoods

    cs.CV 2024-12 reject novelty 6.0 of 10

    A point-based network learns per-event normal flow from raw event camera data and, with IMU data, estimates egomotion; it transfers across datasets better than frame-based optical flow methods.

  7. On-Device Self-Supervised Learning of Low-Latency Monocular Depth from Only Events

    cs.RO 2024-12 conditional novelty 6.0 of 10

    A CUDA-accelerated contrast-maximization pipeline lets a small drone learn monocular depth from events onboard, improving obstacle avoidance compared with pre-training only.

  8. Static in Frames, Dynamic in Events: Rethinking Features in Event Cameras as Motion Cues

    cs.CV 2026-08 conditional novelty 5.0 of 10

    Harris eigenvalues and spatiotemporal density values from event cameras encode motion direction and, when added to an optical flow network, improve accuracy in data-scarce settings.

  9. A Survey of 3D Reconstruction with Event Cameras

    cs.CV 2025-05 conditional novelty 5.0 of 10

    A dedicated survey categorizes event-based 3D reconstruction methods by input setup and reconstruction strategy, and catalogs datasets, metrics, and open challenges.

  10. A Real-Time Event-Based Normal Flow Estimator

    cs.CV 2025-04 conditional novelty 4.0 of 10

    An optimized CUDA implementation computes the same learned event-neighborhood encoding as prior work in linear time, enabling real-time normal flow estimation.

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