From 8e82287f1b7574eee8536468e27153d1cf0c9fcb Mon Sep 17 00:00:00 2001 From: HuguesTHOMAS Date: Mon, 27 Apr 2020 19:01:17 -0400 Subject: [PATCH] Corrections --- doc/scene_segmentation_guide.md | 37 +++++++-------------------------- 1 file changed, 8 insertions(+), 29 deletions(-) diff --git a/doc/scene_segmentation_guide.md b/doc/scene_segmentation_guide.md index ff5f2a1..acc35fc 100644 --- a/doc/scene_segmentation_guide.md +++ b/doc/scene_segmentation_guide.md @@ -1,10 +1,16 @@ - ## Scene Segmentation on S3DIS ### Data -S3DIS dataset can be downloaded here (4.8 GB). 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 here (4.8 GB). +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 here. 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.