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license: apache-2.0 |
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--- |
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# Preparing ISO |
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## Datasets |
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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. |
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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`. |
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However, the *complete dataset generating process* is provided as followed: |
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### OccScanNet |
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1. Clone the official MMDetection3D repository. |
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```bash |
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git clone https://github.com/open-mmlab/mmdetection3d.git ISO |
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``` |
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2. Swith to `v1.3.0` version. |
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```bash |
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cd ISO |
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git checkout v1.3.0 |
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``` |
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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`. |
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> :bulb: Note |
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> |
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> 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. |
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4. In this directory, extract RGB image with poses by running |
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```bash |
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python extract_posed_images.py --max-images-per-scene 100 |
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``` |
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> :bulb: Note |
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> |
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> 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. |
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Then obtained the following directory structure. |
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``` |
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scannet |
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├── meta_data |
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├── posed_images |
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│ ├── scenexxxx_xx |
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│ │ ├── xxxxxx.txt |
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│ │ ├── xxxxxx.jpg |
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│ │ ├── intrinsic.txt |
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├── scans |
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├── batch_load_scannet_data.py |
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├── extract_posed_images.py |
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├── load_scannet_data.py |
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├── README.md |
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├── scannet_utils.py |
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``` |
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5. Download original *CompleteScanNet* |
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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). |
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The ground truth label should be placed as `ISO/data/completescannet/CompleteScanNet_GT`. |
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6. Reformulate *CompleteScanNet* |
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```bash |
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python preprocess_gt.py |
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``` |
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The resulted directory is `ISO/data/completescannet/CompleteScanNet_preprocessed_GT`. |
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Now, we obtained the following directory structure. |
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``` |
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completescannet |
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├── CompleteScanNet_GT |
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│ ├── scenexxxx_xx.ply |
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├── CompleteScanNet_preprocessed_GT |
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│ ├── scenexxxx_xx.npy |
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├── preprocess_gt.py |
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├── visualization.py |
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``` |
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7. Create the *OccScanNet* |
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First, you should create a directories with name `preprocessed_voxels` and `gathered_data` in data disk and link them to the `ISO/data/occscannet`. |
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```bash |
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python generate_gt.py |
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``` |
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``` |
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occscannet |
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├── preprocessed_voxels |
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├── gathered_data |
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├── generate_gt.py |
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├── not_aligns.txt |
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├── wrong_scenes.txt |
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├── bad_scenes.txt |
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├── used_scannames.txt |
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``` |
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Step can be indicated sequentially to make sure each step run correctly. |
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### OccScanNet-mini |
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The scenes we used in OccScanNet-mini is reflected in the config file. |
