# CODED_3D
**Repository Path**: fl9621/CODED_3D
## Basic Information
- **Project Name**: CODED_3D
- **Description**: No description available
- **Primary Language**: Python
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-05-20
- **Last Updated**: 2020-12-19
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
# **3D-CODED** [[Project Page]](http://imagine.enpc.fr/~groueixt/3D-CODED/index.html) [[Paper]](https://arxiv.org/abs/1806.05228) [[Talk]](http://imagine.enpc.fr/~groueixt/3D-CODED/ECCV_6Dpose_Workshop_Groueix.pptx) + **Learning Elementary Structure** [[Project Page]](http://imagine.enpc.fr/~deprellt/atlasnet2/) [[Paper]](https://arxiv.org/abs/1908.04725) [[Code]](https://github.com/TheoDEPRELLE/AtlasNetV2)
**3D-CODED : 3D Correspondences by Deep Deformation**
Thibault Groueix, Matthew Fisher, Vladimir G. Kim , Bryan C. Russell, Mathieu Aubry
In [ECCV, 2018](http://openaccess.thecvf.com/ECCV2018.py).
**Learning elementary structures for 3D shape generation and matching**
Theo Deprelle, Thibault Groueix, Matthew Fisher, Vladimir G. Kim , Bryan C. Russell, Mathieu Aubry
In [Neurips, 2019](https://neurips.cc/Register2?showPanel=2). [Official Code](https://github.com/TheoDEPRELLE/AtlasNetV2)
Learned templates
Faust results
| Method | L2 Train SURREAL | L2 Val SURREAL | Faust Intra results | Faust Inter results |
| ---------------------------------------------------- | --------------- | --------------- | ------------------- | ------------------- |
| **3D-CODED** | 1.098 | 1.315 | 1.747 |2.641 |
| **Points Translation 3D** |9.980 | 1.263 | **1.626** | 2.714 |
| **Patch Deformation 3D** | 1.028| 1.436 |1.742 | **2.578** |
| **Points Translation + Patch Deformation 3D** |**0.969**| **1.173** |1.676 | 2.779 |
| **Points Translation 2D** | 1.09 | 1.54 | 2.054 | 3.005 |
| **Patch Deformation 2D** | 6.354 | 6.767 | 4.46 | 5.420 |
| **Points Translation 10D** | **0.906** | **1.064** | 1.799 | 2.707 |
| **Patch Deformation 10D** | 0.952 | 1.183 | 1.683 | 2.83 |
Sample results
Input : 2 meshes
Task : put them in point-wise correspondence. (suggested by color)
Options (Usually default parameters are good)
```python
# Key parameters
'--dir_name', type=str, default="", help='dirname')
'--inputA', type=str, default = "data/example_0.ply", help='your path to mesh 0'
'--inputB', type=str, default = "data/example_1.ply", help='your path to mesh 1'
# Secondary parameters
'--HR', type=int, default=1, help='Use high Resolution template for better precision in the nearest neighbor step ?'
'--reg_num_steps', type=int, default=3000, help='number of epochs to train for during the regression step'
'--num_points', type=int, default = 6890, help='number of points fed to poitnet'
'--num_angles', type=int, default = 100, help='number of angle in the search of optimal reconstruction. Set to 1, if you mesh are already facing the cannonical direction as in data/example_1.ply'
'--env', type=str, default="CODED", help='visdom environment'
'--clean', type=int, default=0, help='if 1, remove points that dont belong to any edges'
'--scale', type=int, default=0, help='if 1, scale input mesh to have same volume as the template'
'--project_on_target', type=int, default=0, help='if 1, projects predicted correspondences point on target mesh'
'--randomize', type=int, default=0, help='if 1, projects predicted correspondences point on target mesh'
'--LR_input', type=int, default=1, help='Use Low Resolution Input '
```
Results
* **Initial guesses** for *example0* and *example1*:
* **Final reconstruction** for *example0* and *example1*:
On your own meshes
You need to make sure your meshes are preprocessed correctly :
* The meshes are loaded with **Trimesh**, which should support a bunch of formats, but I only tested ```.ply``` files. Good converters include [**Assimp**](https://github.com/assimp/assimp) and [Pymesh](https://github.com/qnzhou/PyMesh).
* The trunk axis is the **Y axis** (visualize your mesh against the mesh in ```data``` to make sure they are normalized in the same way).
* the **scale** should be about 1.7 for a standing human (meaning the unit for the point cloud is the ```m```). You can automatically scale them with the flag ```--scale 1```
--> Failure modes instruction : :warning:
- Sometimes the reconstruction is flipped, which break the correspondences. In the easiest case where you meshes are registered in the same orientation, you can just fix this angle in ```correspondence.py``` line 240, to avoid the flipping problem. Also note from this line that the angle search only looks in [-90°,+90°].
- Check the presence of lonely outliers that break the Pointnet encoder. You could try to remove them with the ```--clean``` flag.
FAUST
* If you want to use ```inference/correspondences.py``` to process a hole dataset, like FAUST test set, you can use ```./inference/script.py```, for the FAUST inter challenge. **Good luck :-)**
Trainer's Options
```python
'--point_translation', type=int, default=0, help='point_translation'
'--dim_template', type=int, default=3, help='dim_template'
'--patch_deformation', type=int, default=0, help='patch_deformation'
'--dim_out_patch', type=int, default=3, help='dim_out_patch'
'--start_from', type=str, default="TEMPLATE", choices=["TEMPLATE, SOUP, TRAINDATA"] ,help='dim_out_patch'
```
Monitor your training on http://localhost:8888/
Note on data preprocessing
The generation process of the dataset is quite heavy so we provide our processed data. Should you want to reproduce the preprocessing, go to ```data/README.md```. Brace yourselve :-)