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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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@ -1,5 +1,5 @@
## Scene Segmentation on S3DIS
## Scene Segmentation on SemanticKitti
### 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_labels.zip` (179 MB)](http://semantic-kitti.org/assets/data_odometry_labels.zip)
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
`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
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