💞 If you find OpenSceneFlow useful to your research, please cite our works 📖 and give a star 🌟 as encouragement. (੭ˊ꒳ˋ)੭✧
OpenSceneFlow is a codebase for point cloud scene flow estimation with pre-trained models and datasets available on HuggingFace.
It is also an official implementation of the following papers:
-
SynFlow: Scaling Up LiDAR Scene Flow Estimation with Synthetic Data
Qingwen Zhang, Xiaomeng Zhu, Chenhan Jiang, Patric Jensfelt
European Conference on Computer Vision (ECCV) 2026
[ Pretrain ] [ Synthesis ] - [ arXiv ] [ Project ]→ here
-
TeFlow: Enabling Multi-frame Supervision for Self-Supervised Feed-forward Scene Flow Estimation
Qingwen Zhang, Chenhan Jiang, Xiaomeng Zhu, Yunqi Miao, Yushan Zhang, Olov Andersson, Patric Jensfelt
Conference on Computer Vision and Pattern Recognition (CVPR) 2026 - Highlight
[ Strategy ] [ Self-Supervised ] - [ arXiv ] [ Project ]→ here
-
DeltaFlow: An Efficient Multi-frame Scene Flow Estimation Method
Qingwen Zhang, Xiaomeng Zhu, Yushan Zhang, Yixi Cai, Olov Andersson, Patric Jensfelt
Conference on Neural Information Processing Systems (NeurIPS) 2025 - Spotlight
[ Backbone ] [ Supervised ] - [ arXiv ] [ Project ]→ here
-
HiMo: High-Speed Objects Motion Compensation in Point Clouds (SeFlow++)
Qingwen Zhang, Ajinkya Khoche, Yi Yang, Li Ling, Sina Sharif Mansouri, Olov Andersson, Patric Jensfelt
IEEE Transactions on Robotics (T-RO) 2025
[ Strategy ] [ Self-Supervised ] - [ arXiv ] [ Project ] → here
-
SeFlow: A Self-Supervised Scene Flow Method in Autonomous Driving
Qingwen Zhang, Yi Yang, Peizheng Li, Olov Andersson, Patric Jensfelt
European Conference on Computer Vision (ECCV) 2024
[ Strategy ] [ Self-Supervised ] - [ arXiv ] [ Project ] → here
-
UniFlow: Zero-Shot LiDAR Scene Flow for Autonomous Vehicles
Siyi Li, Qingwen Zhang, Ishan Khatri, Kyle Vedder, Eric Eaton, Deva Ramanan, Neehar Peri
European Conference on Computer Vision (ECCV) 2026
[ Strategy ] [ Supervised ] - [ arXiv ] [ Project ]→ here
-
DoGFlow: Self-Supervised LiDAR Scene Flow via Cross-Modal Doppler Guidance
Ajinkya Khoche, Qingwen Zhang, Yixi Cai, Sina Sharif Mansouri and Patric Jensfelt
IEEE Robotics and Automation Letters (RA-L) 2026
[ Multi-Model ] [ Self-Supervised ] - [ arXiv ] [ Project ]→ here
-
VoteFlow: Enforcing Local Rigidity in Self-Supervised Scene Flow
Yancong Lin*, Shiming Wang*, Liangliang Nan, Julian Kooij, Holger Caesar
Conference on Computer Vision and Pattern Recognition (CVPR) 2025
[ Backbone ] [ Self-Supervised ] - [ arXiv ] [ Project ] → here
-
Flow4D: Leveraging 4D Voxel Network for LiDAR Scene Flow Estimation
Jaeyeul Kim, Jungwan Woo, Ukcheol Shin, Jean Oh, Sunghoon Im
IEEE Robotics and Automation Letters (RA-L) 2025
[ Backbone ] [ Supervised ] - [ arXiv ] [ Project ] → here
-
SSF: Sparse Long-Range Scene Flow for Autonomous Driving
Ajinkya Khoche, Qingwen Zhang, Laura Pereira Sánchez, Aron Asefaw, Sina Sharif Mansouri and Patric Jensfelt
International Conference on Robotics and Automation (ICRA) 2025
[ Backbone ] [ Supervised ] - [ arXiv ] [ Project ] → here
-
DeFlow: Decoder of Scene Flow Network in Autonomous Driving
Qingwen Zhang, Yi Yang, Heng Fang, Ruoyu Geng, Patric Jensfelt
International Conference on Robotics and Automation (ICRA) 2024
[ Backbone ] [ Supervised ] - [ arXiv ] [ Project ] → here
🎁 One repository, All methods!
Additionally, OpenSceneFlow integrates following excellent works: ICLR’24 ZeroFlow, CVPR’24 ICP-Flow, ICCV’23 FastNSF, RA-L’21 FastFlow3D, NeurIPS’21 NSFP. (More on the way…)
Summary of them:
💡: Want to learn how to add your own network in this structure? Check Contribute section and know more about the code. Fee free to pull request and your bibtex here.
📝: Updates of the codebase and the leaderboard are recorded in CHANGELOG.md.
0. Installation
There are two ways to install the codebase: directly on your local machine or in a Docker container.
Environment Setup
We use conda to manage the environment, you can install it follow here. Then create the base environment with the following command [5~15 minutes]:
git clone --recursive https://github.com/KTH-RPL/OpenSceneFlow.git
cd OpenSceneFlow && conda env create -f environment.yaml
# You may need export your LD_LIBRARY_PATH with env lib
# export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/home/kin/mambaforge/lib
We also provide requirements.txt, please check usage through Dockerfile.
Docker (Recommended for Isolation)
You always can choose Docker which isolated environment and free yourself from installation. Pull the pre-built Docker image or build manually.
# option 1: pull from docker hub
docker pull zhangkin/opensf:full
# run container
docker run -it --net=host --gpus all -v /dev/shm:/dev/shm -v /home/kin/data:/home/kin/data --name opensf zhangkin/opensf:full /bin/zsh
# and better to read your own gpu device info to compile the cuda extension again:
cd /home/kin/workspace/OpenSceneFlow && git pull
cd /home/kin/workspace/OpenSceneFlow/assets/cuda/mmcv && /opt/conda/envs/opensf/bin/python ./setup.py install
cd /home/kin/workspace/OpenSceneFlow/assets/cuda/chamfer3D && /opt/conda/envs/opensf/bin/python ./setup.py install
cd /home/kin/workspace/OpenSceneFlow
conda activate opensf
If you prefer to build the Docker image by yourself, Check build-docker-image section for more details.
1. Data Preparation
Refer to dataprocess/README.md for dataset download instructions. Currently, we support Argoverse 2, Waymo, nuScenes, MAN-TruckScene, ZOD and custom datasets (more datasets will be added in the future).
After downloading, convert the raw data to .h5 format for easy training, evaluation, and visualization. Follow the steps in dataprocess/README.md#process.
For a quick start, use our mini processed dataset, which includes one scene in train and val. It is pre-converted to .h5 format with label data (HuggingFace).
# around 1.3G
wget https://huggingface.co/kin-zhang/OpenSceneFlow/resolve/main/demo-data-v2.zip
unzip demo-data-v2.zip -d /home/kin/data/av2/h5py
Once extracted, you can directly use this dataset to run the training script without further processing.
2. Quick Start
Some tips before running the code:
- Don’t forget to active Python environment before running the code.
- If you want to use wandb, replace all
entity="kth-rpl", to your own entity otherwise tensorboard will be used locally.
- Set correct data path by passing the config, e.g.
train_data=/home/kin/data/av2/h5py/demo/train val_data=/home/kin/data/av2/h5py/demo/val.
And free yourself from trainning, you can download the pretrained weight from HuggingFace - OpenSceneFlow and we provided the detail wget command in each model section. For optimization-based method, it’s train-free so you can directly run with 3. Evaluation (check more in the evaluation section).
conda activate opensf
Supervised Training
DeltaFlow
Train DeltaFlow with the leaderboard submit config. [Runtime: Around 18 hours in 10x RTX 3080 GPUs.]
# total bz then it's 10x2 under above training setup.
python train.py model=deltaflow optimizer.lr=2e-3 epochs=20 batch_size=2 num_frames=5 \
loss_fn=deflowLoss train_aug=True "voxel_size=[0.15, 0.15, 0.15]" "point_cloud_range=[-38.4, -38.4, -3, 38.4, 38.4, 3]" \
optimizer.lr=2e-4 +optimizer.scheduler.name=WarmupCosLR +optimizer.scheduler.max_lr=2e-3 +optimizer.scheduler.warmup_epochs=2
# Pretrained weight can be downloaded through (av2), check all other datasets in the same folder.
wget https://huggingface.co/kin-zhang/OpenSceneFlow/resolve/main/deltaflow/deltaflow-av2.ckpt
Flow4D
Train Flow4D with the leaderboard submit config. [Runtime: Around 18 hours in 4x RTX 3090 GPUs.]
python train.py model=flow4d optimizer.lr=1e-3 epochs=15 batch_size=8 num_frames=5 loss_fn=deflowLoss "voxel_size=[0.2, 0.2, 0.2]" "point_cloud_range=[-51.2, -51.2, -3.2, 51.2, 51.2, 3.2]"
# Pretrained weight can be downloaded through:
wget https://huggingface.co/kin-zhang/OpenSceneFlow/resolve/main/flow4d_best.ckpt
SSF
Extra packages needed for SSF model:
pip install mmengine-lite
pip install torch-scatter -f https://data.pyg.org/whl/torch-2.0.0+cu117.html
Train SSF with the leaderboard submit config. [Runtime: Around 6 hours in 8x A100 GPUs.]
python train.py model=ssf optimizer.lr=8e-3 epochs=25 batch_size=64 loss_fn=deflowLoss "voxel_size=[0.2, 0.2, 6]" "point_cloud_range=[-51.2, -51.2, -3, 51.2, 51.2, 3]"
Pretrained weight can be downloaded through:
# the leaderboard weight
wget https://huggingface.co/kin-zhang/OpenSceneFlow/resolve/main/ssf_best.ckpt
# the long-range weight:
wget https://huggingface.co/kin-zhang/OpenSceneFlow/resolve/main/ssf_long.ckpt
DeFlow
Train DeFlow with the leaderboard submit config. [Runtime: Around 6-8 hours in 4x A100 GPUs.] Please change batch_size&lr accoordingly if you don’t have enough GPU memory. (e.g. batch_size=6 for 24GB GPU)
python train.py model=deflow optimizer.lr=2e-4 epochs=15 batch_size=16 loss_fn=deflowLoss
# Pretrained weight can be downloaded through:
wget https://huggingface.co/kin-zhang/OpenSceneFlow/resolve/main/deflow_best.ckpt
Feed-Forward Self-Supervised Model Training
Train Feed-forward SSL methods (e.g. SeFlow/SeFlow++/VoteFlow etc), we needed to:
- process auto-label process for training. Check dataprocess/README.md#self-supervised-process for more details. We provide these inside the demo dataset already.
- specify the loss function, we set the config here for our best model in the leaderboard.
TeFlow
# [Runtime: Around 20 hours in 10x3080Ti GPUs.]
python train.py model=deltaflow epochs=15 batch_size=2 num_frames=5 train_aug=True \
loss_fn=teflowLoss "voxel_size=[0.15, 0.15, 0.15]" "point_cloud_range=[-38.4, -38.4, -3, 38.4, 38.4, 3]" \
+ssl_label=seflow_auto "+add_seloss={chamfer_dis: 1.0, static_flow_loss: 1.0, dynamic_chamfer_dis: 1.0, cluster_based_pc0pc1: 1.0}" \
optimizer.name=Adam optimizer.lr=2e-3 +optimizer.scheduler.name=StepLR +optimizer.scheduler.step_size=9 +optimizer.scheduler.gamma=0.5
# Pretrained weight can be downloaded through (av2), check all other datasets in the same folder.
wget https://huggingface.co/kin-zhang/OpenSceneFlow/resolve/main/teflow/teflow-av2.ckpt
SeFlow
# [Runtime: Around 11 hours in 4x A100 GPUs.]
python train.py model=deflow optimizer.lr=2e-4 epochs=9 batch_size=16 loss_fn=seflowLoss +ssl_label=seflow_auto "+add_seloss={chamfer_dis: 1.0, static_flow_loss: 1.0, dynamic_chamfer_dis: 1.0, cluster_based_pc0pc1: 1.0}" "model.target.num_iters=2"
# Pretrained weight can be downloaded through:
wget https://huggingface.co/kin-zhang/OpenSceneFlow/resolve/main/seflow_best.ckpt
VoteFlow
Extra pakcges needed for VoteFlow, pytorch3d (prefer 0.7.7) and torch-scatter (prefer 2.1.2):
# Install Pytorch3d
conda install pytorch3d -c pytorch3d
# Install torch-scatter
pip install torch-scatter -f https://data.pyg.org/whl/torch-2.0.0+cu117.html
Train VoteFlow with the leaderboard submit config. [Runtime: Around 32 hours in 4 x V100 GPUs.]
python train.py model=voteflow optimizer.lr=2e-4 +optimizer.scheduler.name=StepLR +optimizer.scheduler.step_size=6 epochs=12 batch_size=4 model.target.m=8 model.target.n=128 loss_fn=seflowLoss "+add_seloss={chamfer_dis: 1.0, static_flow_loss: 1.0, dynamic_chamfer_dis: 1.0, cluster_based_pc0pc1: 1.0}" +ssl_label=seflow_auto
# Pretrained weight can be downloaded through:
wget https://huggingface.co/kin-zhang/OpenSceneFlow/resolve/main/voteflow_best.ckpt
SeFlow++
# [Runtime: Around 10 hours in 4x A100 GPUs.] for Argoverse 2
python train.py model=deflowpp save_top_model=3 val_every=3 voxel_size="[0.2, 0.2, 6]" point_cloud_range="[-51.2, -51.2, -3, 51.2, 51.2, 3]" num_workers=16 epochs=9 optimizer.lr=2e-4 +optimizer.scheduler.name=StepLR "+add_seloss={chamfer_dis: 1.0, static_flow_loss: 1.0, dynamic_chamfer_dis: 1.0, cluster_based_pc0pc1: 1.0}" +ssl_label=seflowpp_auto loss_fn=seflowppLoss num_frames=3 batch_size=4
# Pretrained weight can be downloaded through:
wget https://huggingface.co/kin-zhang/OpenSceneFlow/resolve/main/seflowpp_best.ckpt
Optimization-based Unsupervised Methods
For all optimization-based methods, you can directly run eval.py/save.py to get the result without training, while the running might take really long time, maybe tmux for run it. For multi-program running, the master port can be set through +master_port=12346.
# you can change another model by passing model name.
python eval.py model=fastnsf
# or save the result directly
python save.py model=fastnsf
3. Evaluation
You can view Wandb dashboard for the training and evaluation results or upload result to online leaderboard.
Since in training, we save all hyper-parameters and model checkpoints, the only thing you need to do is to specify the checkpoint path. Remember to set the data path correctly also.
# (feed-forward): load ckpt and run it, it will directly prints all metric
python eval.py checkpoint=/home/kin/seflow_best.ckpt data_mode=val
# (optimization-based): it might need take really long time, maybe tmux for run it.
python eval.py model=nsfp +master_port=12344 # change diff port if you want to have multiple runners.
For online testing, first download multidata-sf-challenge dataset (~150Gb) from HuggingFace and specify the dataset_path in the config. The evaluation result will be saved to a zip file for you to submit to the leaderboard.
# download the dataset for online testing, around 150Gb:
huggingface-cli download kin-zhang/multidata-sf-challenge --local-dir /home/kin/data/multidata-sf-challenge
# eval with results saved to submit to leaderboard
python eval.py checkpoint=/home/kin/seflow_best.ckpt data_mode=test leaderboard_version=3 dataset_path=/home/kin/data/multidata-sf-challenge/challenge_public save_res=True
📊 Range-Wise Metric (New!)
In SSF paper, we introduce a new distance-based evaluation metric for scene flow estimation. Below is an example output for SSF with point_cloud_range to 204.8m and voxel_size=0.2m. Check more long-range result in SSF paper.
| Distance |
Static |
Dynamic |
NumPointsStatic |
NumPointsDynamic |
| 0-35 |
0.00836 |
0.11546 |
3.33e+08 |
1.57e+07 |
| 35-50 |
0.00910 |
0.16805 |
4.40e+07 |
703125 |
| 50-75 |
0.01107 |
0.20448 |
3.25e+07 |
395398 |
| 75-100 |
0.01472 |
0.24133 |
1.31e+07 |
145281 |
| 100-inf |
0.01970 |
0.30536 |
1.32e+07 |
171865 |
| Mean |
0.01259 |
0.20693 |
NaN |
NaN |
Submit result to public leaderboard
To submit your result to the public Leaderboard, if you select data_mode=test, it should be a zip file for you to submit to the leaderboard. Set leaderboard_version in the config to select the format: 3 for the current multi-dataset leaderboard, 1 or 2 for the previous AV2 leaderboards on EvalAI (version 1, version 2), and the results in DeFlow & SeFlow papers are version 1. Leaderboard updates are recorded in CHANGELOG.md.
Register at sceneflow.kth-rpl.se, create an API key on your profile page, then submit:
curl -O https://sceneflow.kth-rpl.se/download/sceneflow_submit.sh
export SCENEFLOW_API_KEY=sfk_xxxxx
bash sceneflow_submit.sh --status # check the key and today's quota
bash sceneflow_submit.sh submission.zip --method-name "MyMethod" --training-data single|multi \
[--project-url URL] [--notes TEXT] [--unsupervised] [--long-range] [--private] [--wait]
# a real example:
bash sceneflow_submit.sh /home/kin/results/deltaflow-891323-e21-test-v3-SynFlow.zip --method-name SynFlow-4k --wait
Run bash sceneflow_submit.sh --help for details of each option, e.g. --long-range publishes the 35-70 m results (overall 0-70 m ranking), leave it off if your model crops the point cloud (e.g. at 51.2 m).
4. Visualization
We provide a script to visualize the results of the model also. You can specify the checkpoint path and the data path to visualize the results. The step is quite similar to evaluation.
# (feed-forward): load ckpt
python save.py checkpoint=/home/kin/seflow_best.ckpt dataset_path=/home/kin/data/av2/preprocess_v2/sensor/vis
# (optimization-based): change another model by passing model name.
python eval.py model=nsfp dataset_path=/home/kin/data/av2/h5py/demo/val
# The output of above command will be like:
Model: DeFlow, Checkpoint from: /home/kin/model_zoo/v2/seflow_best.ckpt
We already write the flow_est into the dataset, please run following commend to visualize the flow. Copy and paste it to your terminal:
python tools/visualization.py vis --res_name 'seflow_best' --data_dir /home/kin/data/av2/preprocess_v2/sensor/vis
Enjoy! ^v^ ------
# Then run the command in the terminal:
python tools/visualization.py vis --res_name 'seflow_best' --data_dir /home/kin/data/av2/preprocess_v2/sensor/vis
https://github.com/user-attachments/assets/f031d1a2-2d2f-4947-a01f-834ed1c146e6
For exporting easy comparsion with ground truth and other methods, we also provided multi-visulization open3d window:
python tools/visualization.py vis --res_name "['flow', 'seflow_best']" --data_dir /home/kin/data/av2/preprocess_v2/sensor/vis
Tips: To quickly create qualitative results for all methods, you can use multiple results comparison mode, select a good viewpoint and then save screenshots for all frames by pressing the P key. You will find all methods’ results saved in the output folder (default: logs/imgs). IrfanView can help you easily crop the images in batch. Enjoy!
Rerun: Another way to interact with rerun, here we vis scene by scene, you can also specify the result name to compare with GT or other methods.
python tools/visualization_rerun.py --scene_file /home/kin/data/av2/h5py/demo/val/25e5c600-36fe-3245-9cc0-40ef91620c22.h5 --res_name "['flow', 'deflow']"
https://github.com/user-attachments/assets/07e8d430-a867-42b7-900a-11755949de21
Cite Us
OpenSceneFlow is originally designed by Qingwen Zhang from DeFlow and SeFlow.
It is actively maintained and developed by the community (ref. below works).
If you find it useful, please cite our works:
@inproceedings{zhang2026teflow,
title = {{TeFlow}: Enabling Multi-frame Supervision for Self-Supervised Feed-forward Scene Flow Estimation},
author = {Zhang, Qingwen and Jiang, Chenhan and Zhu, Xiaomeng and Miao, Yunqi and Zhang, Yushan and Andersson, Olov and Jensfelt, Patric},
year = {2026},
booktitle = {Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
pages = {3667-3676},
}
@article{zhang2026synflow,
title={{SynFlow}: Scaling Up LiDAR Scene Flow Estimation with Synthetic Data},
author={Zhang, Qingwen and Zhu, Xiaomeng and Jiang, Chenhan and Jensfelt, Patric},
journal={arXiv preprint arXiv:2604.09411},
year={2026}
}
@inproceedings{zhang2025deltaflow,
title={{DeltaFlow}: An Efficient Multi-frame Scene Flow Estimation Method},
author={Zhang, Qingwen and Zhu, Xiaomeng and Zhang, Yushan and Cai, Yixi and Andersson, Olov and Jensfelt, Patric},
booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems},
year={2025},
url={https://openreview.net/forum?id=T9qNDtvAJX}
}
@article{zhang2025himo,
title={{HiMo}: High-Speed Objects Motion Compensation in Point Cloud},
author={Zhang, Qingwen and Khoche, Ajinkya and Yang, Yi and Ling, Li and Mansouri, Sina Sharif and Andersson, Olov and Jensfelt, Patric},
journal={IEEE Transactions on Robotics},
year={2025},
volume={41},
pages={5896-5911},
doi={10.1109/TRO.2025.3619042}
}
@inproceedings{zhang2024seflow,
author={Zhang, Qingwen and Yang, Yi and Li, Peizheng and Andersson, Olov and Jensfelt, Patric},
title={{SeFlow}: A Self-Supervised Scene Flow Method in Autonomous Driving},
booktitle={European Conference on Computer Vision (ECCV)},
year={2024},
pages={353–369},
organization={Springer},
doi={10.1007/978-3-031-73232-4_20},
}
@inproceedings{zhang2024deflow,
author={Zhang, Qingwen and Yang, Yi and Fang, Heng and Geng, Ruoyu and Jensfelt, Patric},
booktitle={2024 IEEE International Conference on Robotics and Automation (ICRA)},
title={{DeFlow}: Decoder of Scene Flow Network in Autonomous Driving},
year={2024},
pages={2105-2111},
doi={10.1109/ICRA57147.2024.10610278}
}
And our excellent collaborators works contributed to this codebase also:
@article{li2025uniflow,
title={{UniFlow}: Towards Zero-Shot LiDAR Scene Flow for Autonomous Vehicles via Cross-Domain Generalization},
author={Li, Siyi and Zhang, Qingwen and Khatri, Ishan and Vedder, Kyle and Ramanan, Deva and Peri, Neehar},
journal={arXiv preprint arXiv:2511.18254},
year={2025}
}
@article{khoche2026dogflow,
author={Khoche, Ajinkya and Zhang, Qingwen and Cai, Yixi and Mansouri, Sina Sharif and Jensfelt, Patric},
journal = {IEEE Robotics and Automation Letters},
title = {{DoGFlow}: Self-Supervised LiDAR Scene Flow via Cross-Modal Doppler Guidance},
year = {2026},
volume = {11},
number = {3},
pages = {3836-3843},
doi = {10.1109/LRA.2026.3662592},
}
@article{kim2025flow4d,
author={Kim, Jaeyeul and Woo, Jungwan and Shin, Ukcheol and Oh, Jean and Im, Sunghoon},
journal={IEEE Robotics and Automation Letters},
title={Flow4D: Leveraging 4D Voxel Network for LiDAR Scene Flow Estimation},
year={2025},
volume={10},
number={4},
pages={3462-3469},
doi={10.1109/LRA.2025.3542327}
}
@inproceedings{khoche2025ssf,
title={{SSF}: Sparse Long-Range Scene Flow for Autonomous Driving},
author={Khoche, Ajinkya and Zhang, Qingwen and Sanchez, Laura Pereira and Asefaw, Aron and Mansouri, Sina Sharif and Jensfelt, Patric},
booktitle={2025 IEEE International Conference on Robotics and Automation (ICRA)},
year={2025},
pages={6394-6400},
doi={10.1109/ICRA55743.2025.11128770}
}
@inproceedings{lin2025voteflow,
title={VoteFlow: Enforcing Local Rigidity in Self-Supervised Scene Flow},
author={Lin, Yancong and Wang, Shiming and Nan, Liangliang and Kooij, Julian and Caesar, Holger},
booktitle={CVPR},
year={2025},
}
Thank you for your support! ❤️
Feel free to contribute your method and add your bibtex here by pull request!
❤️: BucketedSceneFlowEval; Pointcept; OpenPCSeg; ZeroFlow …
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