File size: 2,382 Bytes
36b0cc7 aa4abcc 11fc0bc aa4abcc 8af0785 aa4abcc 8af0785 aa4abcc 82a1bf9 11fc0bc 82a1bf9 11fc0bc 8af0785 11fc0bc 8af0785 aa4abcc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 | ---
license: odc-by
language:
- en
viewer: false
---
# ComAsset
ComAsset is the dataset of paper "Beyond the Contact: Discovering Comprehensive Affordance for 3D Objects from Pre-trained 2D Diffusion Models".
The dataset consists of total 83 object meshes, collected from [SketchFab](https://sketchfab.com/).
All of the meshes are converted to `.obj` format with image texture files. We manually canonicalize the objects in terms of location, orientation and scale.
The format of the dataset is as follows:
```
ComAsset
├── data
│ ├── accordion # object category
│ │ └── wx75e99elm1yhyfxz1efg60luadp95sl # object id
│ │ ├── images # folder for texture files
│ │ ├── model.obj
│ │ └── model.mtl
│ ├── axe
│ ├── ...
│ └── watering can
└── categories.json
```
In `categories.json`, you can check the existing object categories, along with the original data URL and the license information.
# License
ComAsset is licensed under the [ODC-By v1.0](https://opendatacommons.org/licenses/by/1-0/) license.
This license applies to the dataset as a whole, and users must also comply with the licenses of individual content.
The license of each content is specified in `categories.json`.
# Loading Dataset
```python
from datasets import load_dataset
from huggingface_hub import snapshot_download
import trimesh
snapshot_dir = snapshot_download(repo_id="SShowbiz/ComAsset", repo_type="dataset")
comasset = load_dataset("SShowbiz/ComAsset", data_files={"metadata": "**/metadata.json"})
with open(os.path.join(snapshot_dir, "categories.json"), "r") as json_file: objects = json.load(json_file)
categories = [object_metadata["category"] for object_metadata in objects]
category, *_ = categories # first category
object_metadata, *_ = comasset['metadata'].filter(lambda example: example['category'] == category) # first object
obj_path = os.path.join(snapshot_dir, object_metadata["obj_file"])
mesh = trimesh.load(obj_path)
```
# Citation
To cite ComA, please use the following BibTeX entry:
```bibtex
@inproceedings{ComA,
title="Beyond the Contact: Discovering Comprehensive Affordance for 3D Objects from Pre-trained 2D Diffusion Models",
author="Kim, Hyeonwoo and Han, Sookwan and Kwon, Patrick and Joo, Hanbyul",
booktitle=ECCV,
year={2024}
}
``` |