| --- |
| license: cc-by-nc-4.0 |
| task_categories: |
| - object-detection |
| tags: |
| - 6d-pose |
| - event-camera |
| - synthetic |
| - blender |
| - gso |
| size_categories: |
| - 100G<n<1T |
| --- |
| |
| # Event6DBlender (Blender-Rendered Training Data — `easy` subset) |
|
|
| Synthetic Blender renders + simulated event streams used to train the depth-extrapolation |
| network behind [Event6D](https://github.com/mickeykang16/Event6D) (CVPR 2026). |
|
|
| This repository hosts the **`easy` subset** that was actually consumed by the released |
| training run (the dataloader hardcodes `categories=['easy']`). A separate companion repo |
| [`mickeykang/Event6DBlenderMedium`](https://huggingface.co/datasets/mickeykang/Event6DBlenderMedium) |
| hosts the `medium` extension — extra data not used by the released checkpoint. |
|
|
| ## Layout |
|
|
| ``` |
| Event6DBlender/ |
| ├── train.txt # full split list (714 easy + 1354 medium) |
| ├── test.txt # 590 sequences (204 easy + 386 medium) |
| ├── gso/<obj_id>/... # 1035 Google Scanned Objects meshes (CC-BY 4.0) |
| ├── EvBlenderProc/25-07-30_easy_9/train_pbr/<seq>/ |
| │ ├── rgb/<frame>.png # 120 frames per sequence, 480×640 RGB |
| │ ├── depth/<frame>.png # 16-bit metric depth (scale in scene_camera.json) |
| │ ├── mask/<frame>_<obj>.png |
| │ ├── mask_visib/<frame>_<obj>.png # visible-object masks (used by dataloader) |
| │ ├── scene_camera.json # per-frame intrinsics K |
| │ ├── scene_gt.json # per-frame (R, t) for every object |
| │ ├── scene_gt_coco.json, scene_gt_info.json |
| └── EvBlenderProcEv/25-07-30_easy_9/<seq>/ |
| └── 0001.npz, 0002.npz, ... # raw events per inter-frame interval |
| # .npz['data'] = struct(x, y, t, p) |
| ``` |
|
|
| Per-sequence: ≈120 RGB frames + 120 depth + ≈30 event npz files. 714 sequences total. |
|
|
| ## Splits |
|
|
| - `train.txt`: 2068 sequences (714 easy + 1354 medium) |
| - `test.txt`: 590 sequences (204 easy + 386 medium) |
|
|
| Released checkpoint uses **`easy` only**. For `medium`, see |
| [`mickeykang/Event6DBlenderMedium`](https://huggingface.co/datasets/mickeykang/Event6DBlenderMedium). |
|
|
| ## Download |
|
|
| ```bash |
| huggingface-cli download mickeykang/Event6DBlender --repo-type dataset \ |
| --local-dir ./data/Event6DBlender |
| ``` |
|
|
| ## Disk-space note |
|
|
| The training pipeline materializes a voxel-grid cache next to the events on first run |
| (`EvBlenderProcEv_cache/`, ≈90 GB across the full split). The cache is deterministic and |
| disposable — delete it any time to reclaim space. |
|
|
| ## Attribution |
|
|
| - **GSO meshes**: [Google Scanned Objects](https://app.gazebosim.org/GoogleResearch/fuel/collections/Scanned%20Objects%20by%20Google%20Research) |
| by Google Research — released under CC-BY 4.0. |
| - Renders produced with [BlenderProc](https://github.com/DLR-RM/BlenderProc); events simulated with [ESIM](https://github.com/uzh-rpg/rpg_esim) (UZH-RPG). |
|
|
| ## 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} |
| } |
| ``` |
|
|
