Python 3.6, Tensorflow 2.6, CUDA 11.4 and cudnn ( /usr/local/cuda-11.4 没有用 conda 的cudatoolkit)
git clone --depth=1 https://github.com/wangzihao77/ASFE-Net.git
conda create -n randlanet python=3.6
conda activate randlanet
pip install tensorflow-gpu==2.6 -i https://pypi.tuna.tsinghua.edu.cn/simple --timeout=120
pip install -r helper_requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple --timeout=120
sh compile_op.sh
ls /home/$USER/data/S3DIS/Stanford3dDataset_v1.2_Aligned_Version
python utils/data_prepare_s3dis.py
ConfigS3DIS:
k_n = 16 # KNN
num_layers = 5 # Number of layers
num_points = 40960 # Number of input points
num_classes = 13 # Number of valid classes
sub_grid_size = 0.04 # preprocess_parameter
batch_size = 6 # batch_size during training
val_batch_size = 20 # batch_size during validation and test
train_steps = 500 # Number of steps per epochs
val_steps = 100 # Number of validation steps per epoch
sub_sampling_ratio = [4, 4, 4, 4, 2] # sampling ratio of random sampling at each layer
d_out = [16, 64, 128, 256, 512] # feature dimension
noise_init = 3.5 # noise initial parameter
max_epoch = 100 # maximum epoch during training
learning_rate = 1e-2 # initial learning rate lr_decays = {i: 0.95 for i in range(0, 500)} # decay rate of learning rate
train_sum_dir = 'train_log'
saving = True
saving_path = None
python -B main_S3DIS.py --gpu 0 --mode train --test_area 1 表示:
Area2-3-4-5-6共228个ply文件作为训练集
Area1共44个ply文件作为验证集
下面为6折交叉验证：
cat jobs_6_fold_cv_s3dis.sh
python -B main_S3DIS.py --gpu 0 --mode train --test_area 1 # Area23456作为训练集，area1作为验证集
python -B main_S3DIS.py --gpu 0 --mode test --test_area 1
python -B main_S3DIS.py --gpu 0 --mode train --test_area 2
python -B main_S3DIS.py --gpu 0 --mode test --test_area 2
python -B main_S3DIS.py --gpu 0 --mode train --test_area 3
python -B main_S3DIS.py --gpu 0 --mode test --test_area 3
python -B main_S3DIS.py --gpu 0 --mode train --test_area 4
python -B main_S3DIS.py --gpu 0 --mode test --test_area 4
python -B main_S3DIS.py --gpu 0 --mode train --test_area 5
python -B main_S3DIS.py --gpu 0 --mode test --test_area 5
python -B main_S3DIS.py --gpu 0 --mode train --test_area 6
python -B main_S3DIS.py --gpu 0 --mode test --test_area 6
## 5 vis
label
python main_S3DIS.py --mode vis --test_area 1
origin
python vis_S3DIS.py
## 6 code
helper_tf_util.py 封装了一些卷积池化操作代码
helper_tool.py 有训练时各个数据集所用到的一些参数信息，还有一些预处理数据时的一些模块。
main_*.py 训练对应数据的主文件
ASFE-Net.py 定义网络的主题结构
tester_*.py 测试对应数据的文件，该文件在main_*.py中被调用
utils 对数据集预处理的模块以及KNN模块。
utils/data_prare_*.py 预处理，把point和label做了grid sampling，并且生成了一个kdtree保存下来
## 7 NET
将特征升维到8
encoder：由4个（dilated_res_block+random_sample）构成，形成特征金字塔
将金字塔尖的特征再次计算以下
decoder：由4个（nearest_interpolation+conv2d_transpose）构成，恢复到point-wise的特征
由point-wise经过一些MLP，得到f_out
### (1) Setup
This code has been tested with Python 3.6, Tensorflow 2.6, CUDA 11.4.0 and cuDNN 7.4.1 on Ubuntu 22.4.
- Clone the repository
git clone --depth=1 https://github.com/wangzihao77/ASFE-Net.git && cd ASFENet
- Setup python environment
conda create -n randlanet python=3.5 source activate randlanet 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](https://drive.google.com/open?id=1iU8yviO3TP87-IexBXsu13g6NklwEkXB).
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
<a href="https://docs.google.com/forms/d/e/1FAIpQLScDimvNMCGhy_rmBA2gHfDu3naktRm6A8BPwAWWDv-Uhm6Shw/viewform?c=0&w=1">here</a>.
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
## Related Repos
1. [SoTA-Point-Cloud: Deep Learning for 3D Point Clouds: A Survey](https://github.com/QingyongHu/SoTA-Point-Cloud) 
2. [SensatUrban: Learning Semantics from Urban-Scale Photogrammetric Point Clouds](https://github.com/QingyongHu/SpinNet) 
3. [3D-BoNet: Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds](https://github.com/Yang7879/3D-BoNet) 
4. [SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration](https://github.com/QingyongHu/SpinNet) 
5. [SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Clouds with 1000x Fewer Labels](https://github.com/QingyongHu/SQN) 