README.md

---
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](https://github.com/mickeykang16/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

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

## Citation

```bibtex
@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}
}
```