Segmentation

ScanNet

Download the data from the ScanNet benchmark. Unzip the data and place it to the folder <scannet_folder>. Enter the subdirectory projects, run the following command to prepare the dataset.
```
python tools/seg_scannet.py --run process_scannet --path_in <scannet_folder>
```
Run the following command to train the network with 4 GPUs. The mIoU on the validation set without voting is 73.7, the training log and weights can be downloaded from this link.
```
python scripts/run_seg_scannet.py --gpu 0,1,2,3 --alias scannet
```
Run the following command to get the per-point predictions for the validation dataset with a voting strategy. And after voting, the mIoU is 74.9 on the validation dataset.
```
python scripts/run_seg_scannet.py --run validate --alias scannet
```
To achieve the 76.2 mIoU on the testing set of the ScanNet benchmark, run the following command to train the network on both the training and validation dataset and get the predictions for the testing dataset.
```
python scripts/run_seg_scannet.py --run train_all --gpu 0,1,2,3 --alias all
python scripts/run_seg_scannet.py --run test --alias all
```

Note

The ocnn-pytorch is very efficient compared with other sparse convolution frameworks. It only takes 18 hours to train the network on ScanNet for 600 epochs with 4 V100 GPUs. For reference, under the same training settings, MinkowskiNet 0.4.3 takes 60 hours and MinkowskiNet 0.5.4 takes 30 hours.

SemanticKITTI

Download the dataset and semantic labels from the official website of SemanticKITTI, including data_odometry_velodyne.zip and data_odometry_labels.zip, and place them into the folder projects/data/SemanticKITTI. Then enter the subdirectory projects, run the following command to prepare the dataset.
```
python tools/seg_kitti.py
```
Run the following command to train the network with 4 GPUs. The mIoU on the validation set is 64.0, the training log and weights can be downloaded from this link.
```
python segmentation.py --config configs/seg_kitti.yaml SOLVER.gpu 0,1,2,3
```

ShapeNet

Run the following command to prepare the dataset.
```
python tools/seg_shapenet.py
```
Run the following command to train the a shallow SegNet with an octree depth of 5 used in the original experiment of O-CNN. And the segmentation refinement based on CRF in the paper is omitted for simplicity. The category mIoU and instance mIoU without voting is 82.5 and 84.0 respectively, the training log and weights can be downloaded from this link. It is also easy to do experiments with an octree depth of 6 by specifying command line parameter --depth 6, with which the category mIoU and instance mIoU is 82.8 and 84.2 respectively.
```
python scripts/run_seg_shapenet.py --depth 5 --model segnet --alias segnet_d5
```
Run the following command to train the a deep UNet with an octree depth of 5. The category mIoU and instance mIoU without voting is 84.2 and 85.4 respectively, the training log and weights can be downloaded from this link.
```
python scripts/run_seg_shapenet.py --depth 5 --model unet --alias unet_d5
```