Official repo for "GMS-3DQA: Projection-based Grid Mini-patch Sampling for 3D Model Quality Assessment", accepted by ACM TOMM.
Get the 6 face projection of the point clouds and replace the folder path in the `train.sh'.
Please refer to MM-PCQA repo for information about generating projections for point clouds. The code is used to generate 4 projections, to modify it for 6 face projections please change the code on line 60-61:
rotate_para = [[0,0],[90*interval,0],[90*interval,0],[90*interval,0]]
for i in range(4):
into:
rotate_para = [[0,0],[90*interval,0],[90*interval,0],[90*interval,0],[0,90*interval],[0,180*interval]]
for i in range(6):
We also provide all the projections of SJTU-PCQA, WPC, and WPC2.0 here on Baiduyunpan. The file structure is like:
├── sjtupcqa_wpc_wpc2.0_6face
│ ├── hhi_0.ply
│ │ ├── 0.png
│ │ ├── 1.png
│ │ ├── 2.png
│ │ ├── 3.png
│ │ ├── 4.png
│ │ ├── 5.png
...
Then use `train.sh' to conduct your own training !
The pretrained `swin_tiny_patch4_window7_224_22k.pth' can be downloaded at here.
If you find our work useful, please give us star and cite our paper as:
@article{zhang2023gms,
title={Gms-3dqa: Projection-based grid mini-patch sampling for 3d model quality assessment},
author={Zhang, Zicheng and Sun, Wei and Wu, Haoning and Zhou, Yingjie and Li, Chunyi and Chen, Zijian and Min, Xiongkuo and Zhai, Guangtao and Lin, Weisi},
journal={ACM Transactions on Multimedia Computing, Communications and Applications},
year={2023},
publisher={ACM New York, NY}
}