grseb9s/3dgnn

3DGNN

This is the Caffe implementation of 3D Graph Neural Networks for RGBD Semantic Segmentation:

Setup

Requirement

Required CUDA (7.0) + Ubuntu14.04.

Installation

For installation, please follow the instructions of Caffe and DeepLab v2.

Data Preparation

1. Download the trained model (https://mycuhk-my.sharepoint.com/:u:/g/personal/1155051740_link_cuhk_edu_hk/ETsf3ekhGbxOp1xYKJxv2hQB8I5OCCui86QLvWvK65_5sw?e=KThQe9).
2. Download the prepared training data (prepared hdf5 data) (https://mycuhk-my.sharepoint.com/:u:/g/personal/1155051740_link_cuhk_edu_hk/EVGJ_xXvtNVCh7spid94AmQB_byhW49i-VH_vqx8oZbrZQ?e=COhKwr).
3. Download the testing data (https://mycuhk-my.sharepoint.com/:u:/g/personal/1155051740_link_cuhk_edu_hk/EVdjeNQqnINOj359HN8WXDgBsouAqSoZC1lRgkSbPNo2hA?e=e0w2sO).
4. Download the original provided data (https://mycuhk-my.sharepoint.com/:u:/g/personal/1155051740_link_cuhk_edu_hk/EZuJHYVcULRNkQ3qm34ugIoBg-69Vprq2POiaat4u5ZLXQ?e=QmWXec).

Usage

1. Clone the repository.

2. Build Caffe and matcaffe:

   cd caffe_code
   make -j8 && make matcaffe

3. Inference:

   • Evaluation code is in folder 'matlabscript'.
   • Download trained models and unzip it. Pretrained model is saved in folder "model/nyu_40/".

   cd matlabscript
   run nyu_crop_data_mask_msc.m

   • The result is saved in folder "../result/nyu_40_msc/"

4. Training:

   • Training data preparation

       cd matlabscript
       run generatedata (setting training = true)
       cd ..
       cd train_data_hdf5_file_generate
       python generate_hdf5
       cd ..

   We have also provided the training data in folder "traindata/"

   • Run caffe training

Citation

If you use our code for research, please cite our paper:

@inproceedings{qi20173d,
  title={3D Graph Neural Networks for RGBD Semantic Segmentation},
  author={Qi, Xiaojuan and Liao, Renjie and Jia, Jiaya and Fidler, Sanja and Urtasun, Raquel},
  booktitle={ICCV},
  year={2017}
}

Question

If you have any question or request about the code and data, please email me at [email protected] . If you need more information for other datasets plesase send email.

License

MIT License