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HuguesTHOMAS 2020-04-27 19:21:01 -04:00
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doc/slam_segmentation_guide.md
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## Scene Segmentation on S3DIS ## Scene Segmentation on SemanticKitti
### Data ### Data
@ -12,22 +12,24 @@ Download the three file named:
* [`data_odometry_calib.zip` (1 MB)](http://www.cvlibs.net/download.php?file=data_odometry_calib.zip) * [`data_odometry_calib.zip` (1 MB)](http://www.cvlibs.net/download.php?file=data_odometry_calib.zip)
* [`data_odometry_labels.zip` (179 MB)](http://semantic-kitti.org/assets/data_odometry_labels.zip) * [`data_odometry_labels.zip` (179 MB)](http://semantic-kitti.org/assets/data_odometry_labels.zip)
uncompress the data and move it to `../../Data/SemanticKitti`. uncompress the data and move it to `../../Data/SemanticKitti`.
http://www.cvlibs.net/download.php?file=data_odometry_velodyne.zip You also need to download the files
[`semantic-kitti-all.yaml`](https://github.com/PRBonn/semantic-kitti-api/blob/master/config/semantic-kitti-all.yaml)
and
[`semantic-kitti.yaml`](https://github.com/PRBonn/semantic-kitti-api/blob/master/config/semantic-kitti.yaml).
Place them in your `../../Data/SemanticKitti` folder.
N.B. If you want to place your data anywhere else, you just have to change the variable N.B. If you want to place your data anywhere else, you just have to change the variable
`self.path` of `S3DISDataset` class ([here](https://github.com/HuguesTHOMAS/KPConv-PyTorch/blob/afa18c92f00c6ed771b61cb08b285d2f93446ea4/datasets/S3DIS.py#L88)). `self.path` of `SemanticKittiDataset` class ([here](https://github.com/HuguesTHOMAS/KPConv-PyTorch/blob/c32e6ce94ed34a3dd9584f98d8dc0be02535dfb4/datasets/SemanticKitti.py#L65)).
### Training ### Training
Simply run the following script to start the training: Simply run the following script to start the training:
python3 training_S3DIS.py python3 training_SemanticKitti.py
Similarly to ModelNet40 training, the parameters can be modified in a configuration subclass called `S3DISConfig`, and the first run of this script might take some time to precompute dataset structures. Similarly to ModelNet40 training, the parameters can be modified in a configuration subclass called `SemanticKittiConfig`, and the first run of this script might take some time to precompute dataset structures.
### Plot a logged training ### Plot a logged training