datasets/README.md

## Download Raw Data
Our final model uses a subset of [Objaverse](https://huggingface.co/datasets/allenai/objaverse) provided by [LGM](https://github.com/ashawkey/objaverse_filter) 

## Data Preprocess
We provide several scripts to preprocess the raw GLB files [here](./preprocess/). These scripts are minimal implementations and illustrate the whole preprocessing pipeline on a single 3D object. 

1. Sample points from mesh surface
```
python datasets/preprocess/mesh_to_point.py --input assets/objects/scissors.glb --output preprocessed_data
```

2. Render images
```
python datasets/preprocess/render.py --input assets/objects/scissors.glb --output preprocessed_data
```

3. Remove background for rendered images and resize to 90%
```
python datasets/preprocess/rmbg.py --input preprocessed_data/scissors/rendering.png --output preprocessed_data
```

4. (Optional) Calculate IoU
```
python datasets/preprocess/calculate_iou.py --input assets/objects/scissors.glb --output preprocessed_data
```
After preprocessing, you can generate a dataset configuration file according to the example configuration file with your own data path. 

To preprocess a folder of meshes, run
```
python datasets/preprocess/preprocess.py --input assets/objects --output preprocessed_data
```
This will also generate a configuration file in `./preprocessed_data/object_part_configs.json`. 

## Dataset Configuration
The training code requires specific format of dataset configuration. I provide an example configuration [here](example_configs.json). You can use it as a template to configure your own dataset. A minimal legal configuration file should be like: 

```
[
    {
        "mesh_path": "/path/to/object.glb",
        "surface_path": "/path/to/object.npy",
        "image_path": "/path/to/object.png",
        "num_parts": 4,
        "iou_mean": 0.5,
        "iou_max": 0.9,
        "valid": true
    }, 
    {
        ...
    },
    ...
]
```
Explaination:
- `mesh_path`: The path to the GLB file of the object.
- `surface_path`: The path to the npy file of the object surface points.
- `image_path`: The path to the rendered image of the object (after removing background).
- `num_parts`: The number of parts of the object.
- `iou_mean`: The mean IoU of the object parts.
- `iou_max`: The max IoU of the object parts.
- `valid`: Whether the object is valid. If set to false, the object will be filtered out during training.