copy from [email protected]:QingyongHu/RandLA-Net.git with some change for private using
(1) Setup This code has been tested with Python 3.5, Tensorflow 1.11, CUDA 9.0 and cuDNN 7.4.1 on Ubuntu 16.04.
Clone the repository git clone --depth=1 https://github.com/QingyongHu/RandLA-Net && cd RandLA-Net Setup python environment conda create -n randlanet python=3.5 source activate randlanet python3 -m pip install -r helper_requirements.txt sh compile_op.sh Update 03/21/2020, pre-trained models and results are available now. You can download the pre-trained models and results here. Note that, please specify the model path in the main function (e.g., main_S3DIS.py) if you want to use the pre-trained model and have a quick try of our RandLA-Net.
(2) S3DIS S3DIS dataset can be found here. Download the files named "Stanford3dDataset_v1.2_Aligned_Version.zip". Uncompress the folder and move it to /data/S3DIS.
Preparing the dataset: python utils/data_prepare_s3dis.py Start 6-fold cross validation: sh jobs_6_fold_cv_s3dis.sh Move all the generated results (*.ply) in /test folder to /data/S3DIS/results, calculate the final mean IoU results: python utils/6_fold_cv.py
(3) Semantic3D 7zip is required to uncompress the raw data in this dataset, to install p7zip:
sudo apt-get install p7zip-full Download and extract the dataset. First, please specify the path of the dataset by changing the BASE_DIR in "download_semantic3d.sh" sh utils/download_semantic3d.sh Preparing the dataset: python utils/data_prepare_semantic3d.py Start training: python main_Semantic3D.py --mode train --gpu 0 Evaluation: python main_Semantic3D.py --mode test --gpu 0