Hugging Face's logo

Datasets:
hongxiaoy
/
OccScanNet

Modalities:
Text
Formats:
text
Size:
10K - 100K
Libraries:
Datasets
License:
Dataset card Data Studio Files
xet
 Community
1
OccScanNet / README.md
hongxiaoy's picture
hongxiaoy
Update README.md
faa2674 verified
preview code
|
raw
history blame
3.16 kB
metadata 
license: apache-2.0

Preparing ISO

Datasets

We provide the OccScanNet dataset files here, 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.
git clone https://github.com/open-mmlab/mmdetection3d.git ISO
  1. Swith to v1.3.0 version.
cd ISO
git checkout v1.3.0
  1. Download the ScanNet dataset following instructions 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.

  1. In this directory, extract RGB image with poses by running
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
  1. Download original CompleteScanNet

The ground truth labels we used are from SCFusion. Ground truth is available at here.

The ground truth label should be placed as ISO/data/completescannet/CompleteScanNet_GT.

  1. Reformulate CompleteScanNet
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
  1. 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.

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.