README.md

---
license: cc-by-nc-4.0
task_categories:
- object-detection
tags:
- 6d-pose
- event-camera
- synthetic
- blender
- gso
size_categories:
- 100G<n<1T
---

# Event6DBlenderMedium (Blender-Rendered Training Data — `medium` extension)

The `medium`-difficulty extension to [`mickeykang/Event6DBlender`](https://huggingface.co/datasets/mickeykang/Event6DBlender).
**Not used by the released [Event6D](https://github.com/mickeykang16/Event6D) checkpoint**
(the public training run hardcodes `categories=['easy']`), but included here for users
who want to extend training to harder sequences.

## Layout

```
Event6DBlenderMedium/
├── EvBlenderProc/25-07-30_medium_9/train_pbr/<seq>/
│   ├── rgb/, depth/, mask/, mask_visib/, scene_camera.json, scene_gt.json, ...
└── EvBlenderProcEv/25-07-30_medium_9/<seq>/0001.npz, 0002.npz, ...
```

1354 training sequences + 386 test sequences (medium-difficulty category).

## Usage

Download into the **same local directory** as `Event6DBlender` so the splits and GSO
meshes from that repo cover the medium sequences as well:

```bash
huggingface-cli download mickeykang/Event6DBlender       --repo-type dataset --local-dir ./data/Event6DBlender
huggingface-cli download mickeykang/Event6DBlenderMedium --repo-type dataset --local-dir ./data/Event6DBlender
```

`train.txt` / `test.txt` from the primary repo already list these sequences.

To train on `easy + medium`, modify the dataloader call (`blender_reader_e2e.py:89`) to
`process_subdir_fast(categories=['easy', 'medium'])`.

## Attribution

Renders use the same GSO mesh set + BlenderProc pipeline as the primary repo — see
[`mickeykang/Event6DBlender`](https://huggingface.co/datasets/mickeykang/Event6DBlender)
for licensing details.

## 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}
}
```