大尺度室外三维点云语义分割

Code for HongKong-3D datasets Semantic Segmentation

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今日得空，将之前的关于大尺度室外三维点云语义分割的工作进行分享，详细代码和训练结果见下面的github链接。欢迎大家关注Star，互相交流。

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GitHub链接：https://github.com/Yangzhouzhou/Large-Scale-MLS-Dataset-Semantic-Segmentation

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Requirements

The code is tested on the following environment

• Python 3.9
• Pytorch 1.12.1
• laspy 2.2.2
• open3d 0.15.0
• matplotlib 3.5.1
• numpy 1.24.2
• pathlib 1.0.1
• CUDA 11.3
• cudnn 8.2.1

Dataset

• Original dataset: HongKong-3D(.las/.laz format)
• Processed dataset: HongKong-3D(.ply format)

Usage

Data Preprocessing

• Using /utils/data_prepare_hongkong.py to preprocess the original dataset.
• Using /utils/generate_augmented_pc.py to generate augmented point clouds.

Training

• Using /main_HongKong.py to train the model. You can try like following (more detail in the code):

python main_HongKong.py --name NAME --log_dir LOG_DIR --max_epoch EPOCH --gpu GPU_ID --vla_split VAL_SPLIT

Testing

• Using /test_HongKong.py to test the model. You can try like following (more detail in the code):

python test_HongKong.py --checkpoint_path CHECKPOINT_PATH --name NAME --log_dir LOG_DIR

config

• Using /helper_tool.py to set the parameters(including bath_size. etc).

Results