https://github.com/angeladai/3DMV

[ECCV'18] 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation
https://github.com/angeladai/3DMV

computer-graphics computer-vision deep-learning scene-understanding

Last synced: 27 days ago
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[ECCV'18] 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation

• Host: GitHub
• URL: https://github.com/angeladai/3DMV
• Owner: angeladai
• License: other
• Created: 2018-07-21T19:51:40.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2022-02-22T14:59:16.000Z (over 2 years ago)
• Last Synced: 2024-04-28T09:32:16.262Z (2 months ago)
• Topics: computer-graphics, computer-vision, deep-learning, scene-understanding
• Language: Python
• Size: 771 KB
• Stars: 207
• Watchers: 13
• Forks: 40
• Open Issues: 5
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Lists

• awesome-SLAM - 3DMV

README

        
# 3DMV

3DMV jointly combines RGB color and geometric information to perform 3D semantic segmentation of RGB-D scans. This work is based on our ECCV'18 paper, [

3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation](https://arxiv.org/pdf/1803.10409.pdf).

[](https://arxiv.org/abs/1803.10409)

## Code

### Installation:  

Training is implemented with [PyTorch](https://pytorch.org/). This code was developed under PyTorch 0.2 and recently upgraded to PyTorch 0.4.

### Training:  

* See `python train.py --help` for all train options. 

Example train call:

```

python train.py --gpu 0 --train_data_list [path to list of train files] --data_path_2d [path to 2d image data] --class_weight_file [path to txt file of train histogram] --num_nearest_images 5 --model2d_path [path to pretrained 2d model]

```

* Trained models: [models.zip](http://kaldir.vc.in.tum.de/adai/3DMV/models.zip)

### Testing

* See `python test.py --help` for all test options. 

Example test call:

```

python test.py --gpu 0 --scene_list test_scenes.txt --model_path models/scannetv2/scannet5_model.pth --data_path_2d [path to 2d image data] --data_path_3d [path to test scene data] --num_nearest_images 5 --model2d_orig_path models/scannetv2/scannet5_model2d.pth

```

### Data:

This data has been precomputed from the [ScanNet](http://www.scan-net.org/) (v2) dataset.

* Train data for ScanNet v2: [3dmv_scannet_v2_train.zip](http://kaldir.vc.in.tum.de/adai/3DMV/data/3dmv_scannet_v2_train.zip) (6.2G)

    * 2D train images can be processed from the ScanNet dataset using the 2d data preparation script in [prepare_data](prepare_data)

    * Expected file structure for 2D data:

    ```

    scene0000_00/

    |--color/

       |--[framenum].jpg

           ⋮

    |--depth/

       |--[framenum].png   (16-bit pngs)

           ⋮

    |--pose/

       |--[framenum].txt   (4x4 rigid transform as txt file)

           ⋮

    |--label/    (if applicable)

       |--[framenum].png   (8-bit pngs)

           ⋮

    scene0000_01/

    ⋮

    ```

* Test scenes for ScanNet v2: [3dmv_scannet_v2_test_scenes.zip](http://kaldir.vc.in.tum.de/adai/3DMV/data/3dmv_scannet_v2_test_scenes.zip) (110M)

## Citation:  

If you find our work useful in your research, please consider citing:

```

@inproceedings{dai20183dmv,

 author = {Dai, Angela and Nie{\ss}ner, Matthias},

 booktitle = {Proceedings of the European Conference on Computer Vision ({ECCV})},

 title = {3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation},

 year = {2018}

}

```

## Contact:

If you have any questions, please email Angela Dai at [email protected].