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DifFlow3D: Toward Robust Uncertainty-Aware Scene Flow Estimation with Iterative Diffusion-Based Refinement

Jiuming Liu, Guangming Wang, Weicai Ye, Chaokang Jiang, Jinru Han, Zhe Liu, Guofeng Zhang, Dalong Du, Hesheng Wang#

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024

📣 News

• [19/Mar/2024] We have released our models and checkpoints based on MSBRN!
• [27/Feb/2024] Our paper has been accepted by CVPR 2024! 🥳🥳🥳

Environments

Our model is trained and tested under:

• Python 3.8.10
• NVIDIA GPU RTX3090 + CUDA CuDNN
• PyTorch (torch == 1.7.1+ cu110)
• scipy
• tqdm
• sklearn
• numba
• cffi
• pypng
• pptk
• thop

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

cd pointnet2
python setup.py install
cd ../

Data preprocess

We adopt the equivalent preprocessing steps in HPLFlowNet and PointPWCNet.

• 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

Evaluation

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

We provide pretrained model in pretrain_weights.

• model_difflow_355_0.0114.pth: checkpoints for flt3d_s and KITTI_s.
• model_difflow_occ_327_0.0428.pth: checkpoints for flt3d_o and KITTI_o.

Please run the following instrcutions for evaluation.

• For flt3d_s and KITTI_s

  python3 evaluate.py config_evaluate.yaml
  

• For flt3d_o and KITTI_o

  python3 evaluate_occ.py config_evaluate_occ.yaml
  

Train

If you want to train from scratch, 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.

• For flt3d_s and KITTI_s

  python3 train_difflow.py config_train.yaml
  

• For flt3d_o and KITTI_o

  python3 train_difflow_occ.py config_train_occ.yaml
  

Quantitative results:

without occlusion

with occlusion

Citation

@inproceedings{liu2024difflow3d,
  title={DifFlow3D: Toward Robust Uncertainty-Aware Scene Flow Estimation with Iterative Diffusion-Based Refinement},
  author={Liu, Jiuming and Wang, Guangming and Ye, Weicai and Jiang, Chaokang and Han, Jinru and Liu, Zhe and Zhang, Guofeng and Du, Dalong and Wang, Hesheng},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={15109--15119},
  year={2024}
}

Acknowledgments

We thank the following open-source project for the help of the implementations:

• PointNet++
• MSBRN