---
license: mit
pretty_name: YCB-V Randomized Renderings (YCB-V-RAND)
task_categories:
- object-detection
tags:
- 6d-pose-estimation
- domain-randomization
- synthetic-data
- ycb-v
viewer: false
---
We provide our physically-based renderings of the YCB-V object dataset randomized YCB-V (YCB-V-RAND) with **randomized texture**, **randomized material**, and **randomized lighting**, allowing to benchmark the influence of such variations on the task of 2D object detection and 6DoF object pose estimation. For more details on the dataset, please take a look on our paper referenced below.  

![Teaser Image](teaser.jpg)

## Contents

Each `<scene_id>.tar.gz` is a WebDataset shard with `train/`, `val/`, `test/` splits (rendered PNGs), plus per-scene `transforms_*.json` (camera transforms), `initial_poses_*_gen.json`, and `test/scene_camera.json` / `test/scene_gt.json` / `test/test_targets.json` (BOP-format annotations).

> Note: some shards contain a couple of empty `initial_poses_*_gen.json` placeholder files (0 bytes, no pose data generated for that split), which breaks the Hub's automatic WebDataset viewer. Download and extract to use — the image/annotation data itself is unaffected.

## Related datasets

- [tpoellabauer/YCB-V-DS](https://huggingface.co/datasets/tpoellabauer/YCB-V-DS) — stereo/depth recordings, same object set
- [tpoellabauer/YCB-V-LUMA](https://huggingface.co/datasets/tpoellabauer/YCB-V-LUMA) — chroma/luma-key recordings
- [tpoellabauer/YCB-LUMA](https://huggingface.co/datasets/tpoellabauer/YCB-LUMA) — luminance-keyed localization follow-up

If you find our work useful, please consider citing our work.  
```
@inproceedings{pollabauer2024generalizing,
  title={Generalizing Neural Radiance Fields for Robust 6D Pose Estimation of Unseen Appearances},
  author={P{\"o}llabauer, Thomas and Wirth, Tristan and Weitz, Paul and Knauthe, Volker and Kuijper, Arjan and Fellner, Dieter W},
  booktitle={International Symposium on Visual Computing},
  pages={300--314},
  year={2024},
  organization={Springer}
}
```