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ForkNet: Multi-branch Volumetric Semantic Completion from a Single Depth Image

The implementation of our paper accepted in ICCV 2019 (International Conference on Computer Vision, IEEE) Yida Wang, David Tan, Nassir Navab and Federico Tombari

ForkNet

road condition

Architecture

training.

Generated synthetic samples

More examples

Data preprocessing

Depth image to TSDF volumes

Firstly you need to go to depth-tsdf folder to compile the our depth converter. Then camake and make are suggested tools to compile our codes.

cmake . # configure
make # compiles demo executable

After the file named with back-project is compiled, depth images of NYU or SUNCG datasets could be converted into TSDF volumes parallelly.

CUDA_VISIBLE_DEVICES=0 python2 data/depth_backproject.py -s /media/wangyida/SSD2T/database/SUNCG_Yida/train/depth_real_png -tv /media/wangyida/HDD/database/SUNCG_Yida/train/depth_tsdf_camera_npy -tp /media/wangyida/HDD/database/SUNCG_Yida/train/depth_tsdf_pcd

Semantic volumes used for training

We further convert the binary files from SUNCG and NYU datasets into numpy arrays in dimension of 804880 with 12 semantic channels. Those voxel data are used as training ground truth.

python2 data/depthbin2npy.py -s /media/wangyida/HDD/database/SUNCGtrain_1001_2000  -tv /media/wangyida/HDD/database/SUNCG_Yida/train/voxel_semantic_npy &
python2 data/depthbin2npy.py -s /media/wangyida/HDD/database/SUNCGtrain_501_1000  -tv /media/wangyida/HDD/database/SUNCG_Yida/train/voxel_semantic_npy &
python2 data/depthbin2npy.py -s /media/wangyida/HDD/database/SUNCGtrain_1_1000  -tv /media/wangyida/HDD/database/SUNCG_Yida/train/voxel_semantic_npy &
python2 data/depthbin2npy.py -s /media/wangyida/HDD/database/SUNCGtrain_1001_3000  -tv /media/wangyida/HDD/database/SUNCG_Yida/train/voxel_semantic_npy &
python2 data/depthbin2npy.py -s /media/wangyida/HDD/database/SUNCGtrain_3001_5000  -tv /media/wangyida/HDD/database/SUNCG_Yida/train/voxel_semantic_npy &
python2 data/depthbin2npy.py -s /media/wangyida/HDD/database/SUNCGtrain_1_500  -tv /media/wangyida/HDD/database/SUNCG_Yida/train/voxel_semantic_npy &
python2 data/depthbin2npy.py -s /media/wangyida/HDD/database/SUNCGtrain_5001_7000  -tv /media/wangyida/HDD/database/SUNCG_Yida/train/voxel_semantic_npy &
python2 data/depthbin2npy.py -s /media/wangyida/HDD/database/depthbin_NYU_SUNCG/SUNCGtest_49700_49884 -tv /media/wangyida/HDD/database/SUNCG_Yida/test/voxel_semantic_npy &
python2 data/depthbin2npy.py -s /media/wangyida/HDD/database/depthbin_NYU_SUNCG/NYUtrain -tv /media/wangyida/HDD/database/NYU_Yida/train/voxel_semantic_npy &
python2 data/depthbin2npy.py -s /media/wangyida/HDD/database/depthbin_NYU_SUNCG/NYUtest -tv /media/wangyida/HDD/database/NYU_Yida/test/voxel_semantic_npy &
wait

Train and Test

Then you can start to train with

CUDA_VISIBLE_DEVICES=0 python3 main.py --mode train --discriminative True

and test with

CUDA_VISIBLE_DEVICES=1 python main.py --mode evaluate_recons --conf_epoch 0

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