scannet.md

ScanNet Segmentation

Data preparation

1. Download the data from the ScanNet benchmark. Unzip the data and place it to the folder <scannet_folder>

2. Change the working directory to pytorch/projects, run the following command to prepare the dataset.

   python tools/scannet.py --run process_scannet --path_in <scannet_folder>

3. Download the training, validation and testing file lists via the following command.

   python tools/scannet.py --run download_filelists
   

Training and testing

1. Run the following command to train the network with 4 GPUs.

   python segmentation.py --config configs/seg_scannet.yaml SOLVER.gpu 0,1,2,3

   The mIoU on the validation set is 74.0, the training log and weights can be downloaded from this link.

2. To achieve the 76.2 mIoU on the testing set, we follow the practice described in the MinkowsiNet, i.e. training the network both on the training and validation set via the following command.

   python segmentation.py --config configs/seg_scannet.yaml SOLVER.gpu 0,1,2,3  \
                          SOLVER.logdir logs/scannet/D9_2cm_all  \
                          DATA.train.filelist data/scannet/scannetv2_train_val_new.txt

   The training log and weights can be downloaded from this link.

3. To generate the per-point predictions, run the following command.

   python segmentation.py --config configs/seg_scannet_eval.yaml

4. Run the following command to convert the predictions to segmentation labels.

   python tools/scannet.py --path_in  data/scannet/test  \
                           --path_pred logs/scannet/D9_2cm_eval  \
                           --path_out logs/scannet/D9_2cm_eval_seg   \
                           --filelist data/scannet/scannetv2_test_new.txt  \
                           --run generate_output_seg

   Then the generated segmentation results exist in the folder logs/scannet/D9_2cm_eval_seg