https://github.com/Yang7879/3D-BoNet

🔥3D-BoNet in Tensorflow (NeurIPS 2019, Spotlight)
https://github.com/Yang7879/3D-BoNet

3d-object-detection 3d-point-clouds 3d-vision computer-vision instance-segmentation

Last synced: 16 days ago
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🔥3D-BoNet in Tensorflow (NeurIPS 2019, Spotlight)

• Host: GitHub
• URL: https://github.com/Yang7879/3D-BoNet
• Owner: Yang7879
• License: mit
• Created: 2019-06-02T22:44:03.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2021-03-02T12:54:20.000Z (over 3 years ago)
• Last Synced: 2024-08-01T03:46:00.220Z (3 months ago)
• Topics: 3d-object-detection, 3d-point-clouds, 3d-vision, computer-vision, instance-segmentation
• Language: Python
• Homepage: https://arxiv.org/abs/1906.01140
• Size: 70.1 MB
• Stars: 392
• Watchers: 11
• Forks: 85
• Open Issues: 54
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
## Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds

Bo Yang, Jianan Wang, Ronald Clark, Qingyong Hu, Sen Wang, Andrew Markham, Niki Trigoni. [arXiv:1906.01140](https://arxiv.org/abs/1906.01140), 2019.

### (1) Setup

ubuntu 16.04 + cuda 8.0

python 2.7 or 3.6

tensorflow 1.2 or 1.4

scipy 1.3

h5py 2.9

open3d-python 0.3.0

#### Compile tf_ops

(1) To find tensorflow include path and library paths:

    import tensorflow as tf

    print(tf.sysconfig.get_include())

    print(tf.sysconfig.get_lib())

(2) To change the path in all the complie files, e.g. tf_ops/sampling/tf_sampling_compile.sh, and then compile:

    cd tf_ops/sampling

    chmod +x tf_sampling_compile.sh

    ./tf_sampling_compile.sh

### (2) Data

S3DIS: [https://drive.google.com/open?id=1hOsoOqOWKSZIgAZLu2JmOb_U8zdR04v0](https://drive.google.com/open?id=1hOsoOqOWKSZIgAZLu2JmOb_U8zdR04v0)

百度盘: [https://pan.baidu.com/s/1ww_Fs2D9h7_bA2HfNIa2ig](https://pan.baidu.com/s/1ww_Fs2D9h7_bA2HfNIa2ig) 密码:qpt7

Acknowledgement: we use the same data released by [JSIS3D](https://github.com/pqhieu/jsis3d).

### (3) Train/test

python main_train.py

python main_eval.py

### (4) Quantitative Results on ScanNet

![Arch Image](https://github.com/Yang7879/3D-BoNet/blob/master/figs/fig_res_scannet.png)

### (5) Qualitative Results on ScanNet

![Arch Image](https://github.com/Yang7879/3D-BoNet/blob/master/figs/fig_ins_scannet.png)

| ![2](./figs/fig_scannet_scene0015.gif)   | ![z](./figs/fig_scannet_scene0081.gif) |

| ---------------------------------------- | -------------------------------------- |

| ![z](./figs/fig_scannet_scene0088.gif)   | ![z](./figs/fig_scannet_scene0196.gif) |

#### More results of ScanNet validation split are available at: [More ScanNet Results](https://drive.google.com/file/d/1cV07rP02Yi3Eu6GQxMR2buigNPJEvCq0/view?usp=sharing)

To visualize:

python helper_data_scannet.py

### (6) Qualitative Results on S3DIS

| ![z](./figs/fig_s3dis_area2_auditorium.gif)   | ![z](./figs/fig_s3dis_area6_hallway1.gif) |

| --------------------------------------------- | ----------------------------------------- |

![Teaser Image](https://github.com/Yang7879/3D-BoNet/blob/master/figs/fig_bb_s3dis.png)

### (7) Training Curves on S3DIS

![Teaser Image](https://github.com/Yang7879/3D-BoNet/blob/master/figs/fig_traincurv_s3dis.png)

### (8) Video Demo (Youtube)

  


### Citation

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

    @inproceedings{yang2019learning,

      title={Learning object bounding boxes for 3d instance segmentation on point clouds},

      author={Yang, Bo and Wang, Jianan and Clark, Ronald and Hu, Qingyong and Wang, Sen and Markham, Andrew and Trigoni, Niki},

      booktitle={Advances in Neural Information Processing Systems},

      pages={6737--6746},

      year={2019}

    }

    

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