Datasets:

mickeykang
/

Event6D

license: cc-by-nc-4.0
task_categories:
  - object-detection
tags:
  - 6d-pose
  - event-camera
  - novel-object
  - rgbd
size_categories:
  - 10G<n<100G

Event6D Dataset (Real-World Capture)

Real-world hand-held capture used as the primary test set in Event6D (CVPR 2026).

Hand-held capture of 14 daily YCB-style objects from random viewpoints and motions.

Hardware

Sensor	Model	Resolution	Rate
RGB-D	Intel RealSense D435i	1280 × 720	30 FPS
Event	Prophesee IMX636	1280 × 720	≥ 5000 FPS

Calibration (intrinsics + RGB↔event extrinsic) is shipped as 0001-camchain.yaml in the Kalibr camchain format.

Layout

Event6D/
├── 0001-camchain.yaml        # Kalibr-format intrinsic + extrinsic calibration
├── test.txt, train.txt       # split lists (sequence frame ranges)
├── simple_mesh/              # 21 YCB-style object meshes (textured.obj + material)
└── <object>_<date>/<run>/    # e.g. banana_1101/0003
    ├── parsed_events/        # raw events as packed .npz per frame (x,y,t,p)
    ├── rgb/                  # color frames at 30 fps
    ├── depth_aligned_to_color/
    ├── depth_aligned_to_event/
    ├── pose/                 # GT object poses (4, 4, 4): 4 sub-timesteps per file
    ├── mask/
    ├── obj.txt               # object id string (e.g. 011_wine_glass)
    ├── startend.txt
    └── aligned_depth_pose.csv

Ground-truth pose format

Although RGB is captured at 30 FPS, the event camera produces a continuous stream that we sample at 120 Hz. To give pose labels at the finer rate, each pose/XXXXXX.npy stores 4 poses instead of one — these are 4 evenly-spaced sub-timesteps that span the interval between RGB frame XXXXXX and the next RGB frame XXXXXX+1.

file shape: (4, 4, 4)
              │  └──┴── 4×4 SE(3) rigid-body transform (object → camera)
              └──── sub-timestep index 0..3 within one 30 FPS frame interval

pose[0] is co-temporal with rgb/XXXXXX.jpg (the start of the interval).
pose[1], pose[2], pose[3] are 1/4, 2/4, 3/4 of the way to rgb/XXXXXX+1.jpg.

Evaluators can therefore choose either:

30 FPS — use only pose[0] per frame, comparing against the tracker's prediction at the RGB instant; or
120 FPS — use all 4 sub-timesteps, comparing against pose predictions emitted at each event sub-step.

Event file timing

Events are pre-packed into parsed_events/XXXXXX.npz. Each file holds the events that occurred during the inter-frame interval ending at RGB frame XXXXXX — i.e. the events between rgb/(XXXXXX-1).jpg and rgb/XXXXXX.jpg (a window of ≈ 1/30 s).

time →
    ┌────────────────── 1 / 30 s ──────────────────┐
    rgb/(i−1).jpg                          rgb/i.jpg
    │                                              │
    │←── parsed_events/i.npz  (events in interval) │
    │                                              │
    ├──── pose/(i−1).npy[0..3] (4 sub-timesteps) ──┤

So for any frame i:

rgb/i.jpg — snapshot at the end of the interval
parsed_events/i.npz — events leading up to that snapshot
pose/(i−1).npy[0..3] — 4 GT poses across the same interval, aligned with the events

Each .npz stores a structured array under key data with fields (x, y, t, p) (event pixel coords, timestamp in seconds, polarity ±1).

Download

huggingface-cli download mickeykang/Event6D --repo-type dataset \
    --local-dir ./data/Event6D

Citation

@inproceedings{kang2026event6d,
  title     = {Event6D: Event-based Novel Object 6D Pose Tracking},
  author    = {Kang, Jae-Young and
               Cho, Hoonehee and
               Lee, Taeyeop and
               Kang, Minjun and
               Wen, Bowen and
               Kim, Youngho and
               Yoon, Kuk-Jin},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year      = {2026}
}