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HuguesTHOMAS 2020-04-27 19:01:17 -04:00
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doc/scene_segmentation_guide.md
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## Scene Segmentation on S3DIS
### Data
S3DIS dataset can be downloaded <a href="https://goo.gl/forms/4SoGp4KtH1jfRqEj2">here (4.8 GB)</a>. Download the file named `Stanford3dDataset_v1.2.zip`, uncompress the folder and move it to `Data/S3DIS/Stanford3dDataset_v1.2`.
We consider our experiment folder is located at `XXXX/Experiments/KPConv-PyTorch`. And we use a common Data folder
loacated at `XXXX/Data`. Therefore the relative path to the Data folder is `../../Data`.
S3DIS dataset can be downloaded <a href="https://goo.gl/forms/4SoGp4KtH1jfRqEj2">here (4.8 GB)</a>.
Download the file named `Stanford3dDataset_v1.2.zip`, uncompress the data and move it to `../../Data/S3DIS`.
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)).
### Training
@ -15,33 +21,6 @@ Simply run the following script to start the training:
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.
## Scene Segmentation on Scannet
Incoming
## Scene Segmentation on Semantic3D
### Data
Semantic3D dataset can be found <a href="http://www.semantic3d.net/view_dbase.php?chl=2">here</a>. Download and unzip every point cloud as ascii files and place them in a folder called `Data/Semantic3D/original_data`. You also have to download and unzip the groundthruth labels as ascii files in the same folder
### Training
Simply run the following script to start the training:
python3 training_Semantic3D.py
Similarly to ModelNet40 training, the parameters can be modified in a configuration subclass called `Semantic3DConfig`, and the first run of this script might take some time to precompute dataset structures.
## Scene Segmentation on NPM3D
Incoming
## Plot and test trained models
### Plot a logged training
When you start a new training, it is saved in a `results` folder. A dated log folder will be created, containing many information including loss values, validation metrics, model checkpoints, etc.