Wencan Cheng and Jong Hwan Ko

Our model is trained and tested under:

• Python 3.6.9
• NVIDIA GPU + CUDA CuDNN
• PyTorch (torch == 1.6.0)
• scipy
• tqdm
• sklearn
• numba
• cffi
• pypng
• pptk
• thop

Please follow this repo or the instructions below for compiling the furthest point sampling, grouping and gathering operation for PyTorch.

cd pointnet2
python setup.py install
cd ../

We adopt the equivalent preprocessing steps in HPLFlowNet and PointPWCNet.

We copy the preprocessing instructions here for your convinience.

• FlyingThings3D: Download and unzip the "Disparity", "Disparity Occlusions", "Disparity change", "Optical flow", "Flow Occlusions" for DispNet/FlowNet2.0 dataset subsets from the FlyingThings3D website (we used the paths from this file, now they added torrent downloads) . They will be upzipped into the same directory, RAW_DATA_PATH. Then run the following script for 3D reconstruction:

python3 data_preprocess/process_flyingthings3d_subset.py --raw_data_path RAW_DATA_PATH --save_path SAVE_PATH/FlyingThings3D_subset_processed_35m --only_save_near_pts

• KITTI Scene Flow 2015 Download and unzip KITTI Scene Flow Evaluation 2015 to directory RAW_DATA_PATH. Run the following script for 3D reconstruction:

python3 data_preprocess/process_kitti.py RAW_DATA_PATH SAVE_PATH/KITTI_processed_occ_final

Set data_root in the configuration file to SAVE_PATH in the data preprocess section before evaluation.

We provide pretrained model in pretrain_weights.

Please run the following instrcutions for evaluating.

python3 evaluate_bid_pointconv.py config_evaluate_bid_pointconv.yaml

If you need a newly trained model, please set data_root in the configuration file to SAVE_PATH in the data preprocess section before evaluation at the first. Then excute following instructions.

python3 train_bid_pointconv.py config_train_bid_pointconv.yaml

We thank repo for the corase-to-fine framework.