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matlab
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README.md
README.md
 
 

README.md

Prepare SUN RGB-D Data

We follow the procedure in votenet.

  1. Download SUNRGBD v2 data HERE. Then, move SUNRGBD.zip, SUNRGBDMeta2DBB_v2.mat, SUNRGBDMeta3DBB_v2.mat and SUNRGBDtoolbox.zip to the OFFICIAL_SUNRGBD folder, unzip the zip files.

  2. Enter the matlab folder, Extract point clouds and annotations by running extract_split.m, extract_rgbd_data_v2.m and extract_rgbd_data_v1.m.

  3. Enter the project root directory, Generate training data by running

python tools/create_data.py sunrgbd --root-path ./data/sunrgbd --out-dir ./data/sunrgbd --extra-tag sunrgbd

The overall process could be achieved through the following script

cd matlab
matlab -nosplash -nodesktop -r 'extract_split;quit;'
matlab -nosplash -nodesktop -r 'extract_rgbd_data_v2;quit;'
matlab -nosplash -nodesktop -r 'extract_rgbd_data_v1;quit;'
cd ../../..
python tools/create_data.py sunrgbd --root-path ./data/sunrgbd  --out-dir ./data/sunrgbd --extra-tag sunrgbd

NOTE: SUNRGBDtoolbox.zip should have MD5 hash 18d22e1761d36352f37232cba102f91f (you can check the hash with md5 SUNRGBDtoolbox.zip on Mac OS or md5sum SUNRGBDtoolbox.zip on Linux)

The directory structure after pre-processing should be as below

sunrgbd
├── README.md
├── matlab
│   ├── extract_rgbd_data_v1.m
│   ├── extract_rgbd_data_v2.m
│   ├── extract_split.m
├── OFFICIAL_SUNRGBD
│   ├── SUNRGBD
│   ├── SUNRGBDMeta2DBB_v2.mat
│   ├── SUNRGBDMeta3DBB_v2.mat
│   ├── SUNRGBDtoolbox
├── sunrgbd_trainval
│   ├── calib
│   ├── image
│   ├── label_v1
│   ├── train_data_idx.txt
│   ├── depth
│   ├── label
│   ├── seg_label
│   ├── val_data_idx.txt
├── points
├── sunrgbd_infos_train.pkl
├── sunrgbd_infos_val.pkl