An unofficial pyotrch implement of 3D-RecGAN (ICCV Workshops 2017)

The official implement (python2.7+tensorflow):https://github.com/Yang7879/3D-RecGAN

Bo Yang, Hongkai Wen, Sen Wang, Ronald Clark, Andrew Markham, Niki Trigoni. In ICCV Workshops, 2017.

https://arxiv.org/abs/1708.07969

1. Requirements

• Python >= 3.5 (3.6 recommended)
• Training : pytorch>=1.0
• torchvision>=0.4.0
• tqdm

2. Process

3. Re-implement visualization

Input: Ground Truth: 3D-RecAE(L1 loss): 3D-RecAE(CE loss): 3D-RecGAN:

4. Data(Provided by author)

https://drive.google.com/open?id=1n4qQzSd_S6Isd6WjKD_sq6LKqn4tiQm9

Data are also available at Baidu Pan:

https://pan.baidu.com/s/165IXaA_JISCwGzTUCiuPig 提取码: gbp2

5. Run

5.1 Train

python train.py -c config.json

5.2 Test

python test.py -c config.json

5.3 Gif visualization

1. Transfer the generated ply file to off format. (Recommend meshlab)
2. Open the unity project in visualization dir.
3. Modify the Data Path in Point Cloud Manager script of Main Camera. (PS: relative path to assert dir)
4. Run the unity project and the projected images around 3D object will be saved.
5. Transfer the saved images to gif. (Recommend https://www.iloveimg.com/)

6. Experiments

6.1 Per-category IoU and CE Loss

6.1.1 Results in paper :

	IOU			CE Loss
trained/tested on	chair	stool	toilet	chair	stool	toilet
3D-RecAE(CE loss)	0.633	0.488	0.520	0.069	0.085	0.166
3D-RecGAN	0.661	0.501	0.569	0.074	0.083	0.157

6.1.2 Re-implement:

	IOU			CE Loss
trained/tested on	chair	stool	toilet	chair	stool	toilet
3D-RecAE(CE loss)	0.5931	*	*	0.0547	*	*
3D-RecAE(L1 loss)	0.5171	*	*	0.4769	*	*
3D-RecGAN	0.4478	*	*	0.0805	*	*

7. Todo

• RecGAN : Per-category IoU and CE Loss
• RecAE : Per-category IoU and CE Loss
• Multi-category IoU and CE Loss
• Cross-category IoU and CE Loss

8. Reference repo

• https://github.com/Yang7879/3D-RecGAN
• https://github.com/wolny/pytorch-3dunet
• https://github.com/moemen95/Pytorch-Project-Template