Update README.md

README.md

@@ -1,3 +1,102 @@
----
-license: apache-2.0
----
+---
+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`.
+
+The following is the complete dataset generating process.
+
+### OccScanNet
+
+1. Clone the official MMDetection3D repository.
+
+```bash
+git clone https://github.com/open-mmlab/mmdetection3d.git ISO
+```
+
+2. Swith to `v1.3.0` version.
+
+```bash
+cd ISO
+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/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/data/completescannet/CompleteScanNet_GT`.
+
+6. Reformulate *CompleteScanNet*
+
+```bash
+python preprocess_gt.py
+```
+
+The resulted directory is `ISO/data/completescannet/CompleteScanNet_preprocessed_GT`.
+
+Now, we obtained the following directory structure.
+
+```
+completescannet
+├── CompleteScanNet_GT
+│   ├── scenexxxx_xx.ply
+├── CompleteScanNet_preprocessed_GT
+│   ├── 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/data/occscannet`.
+
+```bash
+python generate_gt.py --step [1, 2, 3, 4, 5]
+```
+
+Step can be indicated sequentially to make sure each step run correctly.
+
+### OccScanNet-mini
+
+The scenes we used in OccScanNet-mini is reflected in the config file.