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---
license: apache-2.0
---
# Preparing ISO
## Datasets
We provide the OccScanNet dataset files [here](https://huggingface.co/datasets/hongxiaoy/OccScanNet/tree/main), but you should agree the term of use of *ScanNet*, *CompleteScanNet* dataset.
For **a simplified way** to prepare the dataset, you **just** download the `preprocessed_data` to `ISO/data/occscannet` as `gathered_data` and download the `posed_images` to `ISO/data/scannet`.
However, the *complete dataset generating process* is provided as followed:
### OccScanNet
1. Clone the official MMDetection3D repository.
```bash
git clone https://github.com/open-mmlab/mmdetection3d.git ISO_mm
```
2. Swith to `v1.3.0` version.
```bash
cd ISO_mm
git checkout v1.3.0
```
3. Download the *ScanNet* dataset following [instructions](https://github.com/open-mmlab/mmdetection3d/tree/v1.3.0/data/scannet) and place `scans` directory as `ISO_mm/data/scannet/scans`.
> :bulb: Note
>
> Recommend you create a `posed_images` directory at data disk and link the `scans` directory and `posed_images` directory to `data/scannet`, then run the following command.
4. In this directory, extract RGB image with poses by running
```bash
python extract_posed_images.py --max-images-per-scene 100
```
> :bulb: Note
>
> Add `--max-images-per-scene -1` to disable limiting number of images per scene. ScanNet scenes contain up to 5000+ frames per each. After extraction, all the .jpg images require 2 Tb disk space. The recommended 300 images per scene require less then 100 Gb. For example multi-view 3d detector ImVoxelNet samples 50 and 100 images per training and test scene.
Then obtained the following directory structure.
```
scannet
├── meta_data
├── posed_images
│ ├── scenexxxx_xx
│ │ ├── xxxxxx.txt
│ │ ├── xxxxxx.jpg
│ │ ├── intrinsic.txt
├── scans
├── batch_load_scannet_data.py
├── extract_posed_images.py
├── load_scannet_data.py
├── README.md
├── scannet_utils.py
```
5. Download original *CompleteScanNet*
The ground truth labels we used are from [SCFusion](https://github.com/ShunChengWu/SCFusion#generate-gt). Ground truth is available at [here](https://github.com/ShunChengWu/SCFusion#generate-gt).
The ground truth label should be placed as `ISO_mm/data/completescannet/gt`.
6. Reformulate *CompleteScanNet*
```bash
python preprocess_gt.py
```
The resulted directory is `ISO_mm/data/completescannet/preprocessed`.
Now, we obtained the following directory structure.
```
completescannet
├── gt
│ ├── scenexxxx_xx.ply
├── preprocessed
│ ├── scenexxxx_xx.npy
├── preprocess_gt.py
├── visualization.py
```
7. Create the *OccScanNet*
First, you should create a directories with name `preprocessed_voxels` and `gathered_data` in data disk and link them to the `ISO_mm/data/occscannet`.
```bash
python generate_gt.py
```
Now, we obtained the following directory structure.
```
occscannet
├── preprocessed_voxels
├── gathered_data
├── generate_gt.py
├── not_aligns.txt
├── wrong_scenes.txt
├── bad_scenes.txt
├── used_scannames.txt
```
### OccScanNet-mini
The scenes we used in OccScanNet-mini is reflected in the config file. |