preprocess/README.md

## Data Processing

We have released the preprocessing scripts for 3RScan, MultiScan, ARKitScenes and Structured3D. They are designed to provide a comprehensive framework for data preparation. Taking 3RScan as an example, the process involves the following steps:

- Import raw meshes and annotations from each dataset.  
- Extract vertices from the mesh and assign both instance and semantic labels to them.  
- Map the dataset-specific semantic labels to ScanNet 607. This is optional for SceneVerse training but may be required for closed-vocab training ([example](https://github.com/scene-verse/SceneVerse/blob/b936f96b61614bec32282e5eed7de844d1a7a330/preprocess/rscan.py#L58)).
- Axis Alignment: Rotate the 3D point clouds so that most 3D object bounding boxes are axis-aligned. This follows ScanRefer, and is currently implemented as a heuristic search ([example](https://github.com/scene-verse/SceneVerse/blob/b936f96b61614bec32282e5eed7de844d1a7a330/preprocess/rscan.py#L95)).  
- Translation Alignment: Translate the 3D point clouds so that its origin at the center on the floor ([example](https://github.com/scene-verse/SceneVerse/blob/b936f96b61614bec32282e5eed7de844d1a7a330/preprocess/rscan.py#L102)).  
- Color Alignment: The color value should be within the [0, 255] range ([example](https://github.com/scene-verse/SceneVerse/blob/b936f96b61614bec32282e5eed7de844d1a7a330/preprocess/rscan.py#L98)).
- Point subsampling: subsample the point clouds if the number of points exceeds 240K.
    ```python
    PTS_LIMIT = 240000
    if out_points.shape[0] > PTS_LIMIT:
        pcd_idxs = np.random.choice(out_points.shape[0], size=PTS_LIMIT, replace=False)
        out_points = out_points[pcd_idxs]
        out_colors = out_colors[pcd_idxs]
        instance_labels = instance_labels[pcd_idxs]
    ```

The detailed steps may vary between datasets. Please note the translation and color alignment are critical as they can significantly impact performance. Axis alignment, which requires 3D bounding box annotations, may result in slight but not severe degradation.

### 3RScan
To reproduce the data preprocessing, download [3RScan](https://waldjohannau.github.io/RIO/) and run:
```shell
# Preprocess 3RScan 
$ python rscan.py
```
Adjust the `data_root`, `save_root` and `num_workers` accordingly.

### HM3D
As some of our users requested the mapping between HM3D object id in SceneVerse to HM3D-semantics, we have added an additional file ([HM3D_tgtID2objID.zip](assets/HM3D_tgtID2objID.zip)) to obtain this mapping. The json file for each scene contains a dictionary of ```{<sceneverse_objid>:[hm3d_objid, hm3d_label]}```.
* Note: The script ```sceneverse2hmsemantic.py``` has been deprecated as it cannot reproduce the mappings above. It currently points out how we read the semantics from the annotations in HM3D-semantics.


## Prepare for your custom datasets
To prepare your custom data for inference, follow the previous steps and  the example script for 3RScan. A convenient way for verification is to use the `visualize_data.py`. If everything is correct, you should observe the colored point clouds displayed similarly to those in the released version of SceneVerse.

## Scene Graph Generation
We also release the [scripts](preprocess/ssg/README.md) for 3D scene graph generation.