We follow the procedure in votenet.
-
Download SUNRGBD data HERE. Then, move SUNRGBD.zip, SUNRGBDMeta2DBB_v2.mat, SUNRGBDMeta3DBB_v2.mat and SUNRGBDtoolbox.zip to the OFFICIAL_SUNRGBD folder, unzip the zip files.
-
Enter the
matlab
folder, Extract point clouds and annotations by runningextract_split.m
,extract_rgbd_data_v2.m
andextract_rgbd_data_v1.m
. -
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)
NOTE: If you would like to play around with ImVoteNet, the image data (./data/sunrgbd/sunrgbd_trainval/image
) are required. If you pre-processed the data before mmdet3d version 0.12.0, please pre-process the data again due to some updates in data pre-processing
python tools/create_data.py sunrgbd --root-path ./data/sunrgbd --out-dir ./data/sunrgbd --extra-tag sunrgbd
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
│ ├── depth
│ ├── image
│ ├── label
│ ├── label_v1
│ ├── seg_label
│ ├── train_data_idx.txt
│ ├── val_data_idx.txt
├── points
├── sunrgbd_infos_train.pkl
├── sunrgbd_infos_val.pkl