DiffSF: Diffusion Models for Scene Flow Estimation

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DiffSF: Diffusion Models for Scene Flow Estimation Yushan Zhang, Bastian Wandt, Maria Magnusson, Michael Felsberg
Arxiv 2024

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Installation:

Create a conda environment:

conda create -n diffsf python=3.9
conda activate diffsf
conda install pytorch torchvision pytorch-cuda=11.8 -c pytorch -c nvidia

Install other dependencies:

pip install tqdm tensorboard opencv-python

Dataset Preparation:

1. FlyingThings3D(HPLFlowNet without occlusion / CamLiFlow with occlusion):

Download FlyingThings3D_subset. flyingthings3d_disparity.tar.bz2, flyingthings3d_disparity_change.tar.bz2, FlyingThings3D_subset_disparity_occlusions.tar.bz2, FlyingThings3D_subset_flow.tar.bz2, FlyingThings3D_subset_flow_occlusions.tar.bz2 and FlyingThings3D_subset_image_clean.tar.bz2 are needed. Then extract the files in /path/to/flyingthings3d such that the directory looks like

/path/to/flyingthings3d
├── train/
│   ├── disparity/
│   ├── disparity_change/
│   ├── disparity_occlusions/
│   ├── flow/
│   ├── flow_occlusions/
│   ├── image_clean/
├── val/
│   ├── disparity/
│   ├── disparity_change/
│   ├── disparity_occlusions/
│   ├── flow/
│   ├── flow_occlusions/
│   ├── image_clean/

Preprocess dataset using the following command:

cd utils
python preprocess_flyingthings3d_subset.py --input_dir /mnt/data/flyingthings3d_subset --output_dir flyingthings3d_subset
python preprocess_flyingthings3d_subset.py --input_dir /mnt/data/flyingthings3d_subset --output_dir flyingthings3d_subset_non-occluded --remove_occluded_points

2. FlyingThings3D(FlowNet3D with occlusion):

The processed data is also provided here for download (total size ~11GB)

3. KITTI(HPLFlowNet without occlusion):

First, download the following parts: Main data: data_scene_flow.zip Calibration files: data_scene_flow_calib.zip Unzip them and organize the directory as follows:

datasets/KITTI_stereo2015
├── testing
│   ├── calib_cam_to_cam
│   ├── calib_imu_to_velo
│   ├── calib_velo_to_cam
│   ├── image_2
│   ├── image_3
└── training
    ├── calib_cam_to_cam
    ├── calib_imu_to_velo
    ├── calib_velo_to_cam
    ├── disp_noc_0
    ├── disp_noc_1
    ├── disp_occ_0
    ├── disp_occ_1
    ├── flow_noc
    ├── flow_occ
    ├── image_2
    ├── image_3
    ├── obj_map

Preprocess dataset using the following command:

cd utils
python process_kitti.py datasets/KITTI_stereo2015/ SAVE_PATH/KITTI_processed_occ_final

4. KITTI(FlowNet3D with occlusion):

The processed data is also provided here for download

5. Waymo-Open(refer to FH-Net): Download the Waymo raw data from link_to_waymo_open_dataset,run the following command to extract point clouds, 3D annotations, poses and other information form raw data.

cd diffsf
python waymo_tools/waymo_extract.py

After extracting data, the folder structure is the same as below:

datasets
├── waymo-open
│   ├── scene_flow
│       ├── ImageSets
│       ├── train
│       ├── valid
├── train_extract
│   ├── 000
│   ├── 001
│   ├── ...
├── valid_extract
│   ├── 000
│   ├── 001
│   ├── ...

Then create scene flow data by:

python waymo_tools/create_data.py --dataset_type waymo

The datasets directory should be orginized as:

datasets
├── datasets_KITTI_flownet3d
│   ├── kitti_rm_ground
├── datasets_KITTI_hplflownet
│   ├── KITTI_processed_occ_final
├── FlyingThings3D_flownet3d
├── flyingthings3d_subset
│   ├── train
│   ├── val
├── flyingthings3d_subset_non-occluded
│   ├── train
│   ├── val
├── KITTI_stereo2015
│   ├── testing
│   ├── training
├── waymo-open
│   ├── scene_flow
│   ├── train_extract
│   ├── valid_extract

Traning and Testing:

1-FlyThings3D_occ Training (FlyThings3D_occ):

python diffsf_main.py --train_dataset f3d_occ --lr 4e-4 --train_batch_size 24

Testing (FlyThings3D_occ):

python diffsf_main.py --val_dataset f3d_occ --eval
Testing (KITTI_occ): 

python diffsf_main.py --val_dataset kitti_occ --eval

2-FlyThings3D_nonocc Training (FlyThings3D_nonocc):

python diffsf_main.py --train_dataset f3d_nonocc --lr 4e-4 --train_batch_size 24

Testing (FlyThings3D_nonocc):

python diffsf_main.py --val_dataset f3d_nonocc --eval
Testing (KITTI_nonocc): 

python diffsf_main.py --val_dataset kitti_nonocc --eval

3-Waymo-Open Training (Waymo-Open):

python diffsf_main.py --train_dataset waymo --lr 1e-4 --train_batch_size 24

Testing (Waymo-Open):

python diffsf_main.py --val_dataset waymo --eval

Pretrained Checkpoints

1-FlyThings3D_occ 2-FlyThings3D_nonocc 3-Waymo-Open

BibTeX

If you find our models useful, please consider citing our paper!

@article{zhang2024diffsf,
  title={DiffSF: Diffusion Models for Scene Flow Estimation},
  author={Zhang, Yushan and Wandt, Bastian and Magnusson, Maria and Felsberg, Michael},
  journal={arXiv preprint arXiv:2403.05327},
  year={2024}
}