Open source code of the paper: 3DTA: No-Reference 3D Point Cloud Quality Assessment with Twin Attention

1. Download Code Download this github repository to your computer, with the following folder structure:

———— 📁 code

———————— 🐍 1.1pc_to_patch.py

———————— 🐍 1.2patch_list_create.py

———————— 🐍 1.3main.py

———————— 🐍 data_load.py

———————— 🐍 model_3DTA.py

———————— 🐍rename_error_file.py

———————— 🐍 util.py

———— 📁 data

———————————— 🔢 mos.xls

———————————— 🔢 test.xls

———————————— 🔢 train.xls

———— 📁 images

———— 📰 README.md

2. Data Preparation Download the WPC datasets from https://github.com/qdushl/Waterloo-Point-Cloud-Database, and copy all the distorted 740 ply files into ./data/WPC/Distortion_ply folder. All files are in the same folder. We have prepared the dataset segmentation file: mos.xls、test.xls、train.xls.

3. Install Dependencies Please install CUDA and cudnn in advance. Our code can only run on GPU at present. In addition, Anaconda is recommended. Python >= 3.8 is required, and the Python libraries that need to be installed are as follows:

torch

tqdm

xlrd

argparse

numpy

pandas

plyfile

multiprocessing

sklearn

scipy

open3d

The above Python libraries are sufficient as long as they do not conflict with each other and do not require specific versions.

4. Run Code Run the code one by one to obtain the experimental results:

1.pc_to_patch.py

2.patch_list_create.py

3.main.py