conf: update deltaflow conf files

update README to show the progress.

Author: Kin-Zhang

README.md

@@ -6,28 +6,50 @@ DeltaFlow
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-Status: Under Review 🚀
-
-We will release the full codebase, trained models, supplementary materials and all baselines upon acceptance and publication.
-
-Stay tuned and feel free to star ⭐ this repository to get notified when we publish.
-
-
-🔗 While you wait, explore our codebase: [KTH-RPL/OpenSceneFlow](https://github.com/KTH-RPL/OpenSceneFlow).
-
 **News w. TBD**:
 
+Note (2025/09/18): We got accepted by NeurIPS 2025 and it's **spotlighted**! 🎉🎉🎉 Working on release the code here.
+
 - 2025/08/24: I'm updating some codes for early release. 
 - [x] 2025/08/24: Updating train data augmentation as illustrated in the DeltaFlow paper.
 - [x] 2025/08/25: Updating paper preprint link.
 - [x] 2025/09/05: Merged the latest commit from OpenSceneFlow codebase to DeltaFlow for afterward unified merged.
-- [ ] DeltaFlow Model python file.
+- [x] 2025/09/25: DeltaFlow Model python file and config file.
 - [ ] pre-trained weights upload.
 - [ ] DeltaFlow Loss fn.
 - [ ] Merged into [OpenSceneFlow](https://github.com/KTH-RPL/OpenSceneFlow)
 
-## Cite & Acknowledgements
+## Quick Run
+
+### Training
+
+1. Prepare the demo train and val data:
+```bash
+# 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 # to your data path
+```
+
+2. Follow the [OpenSceneFlow](https://github.com/KTH-RPL/OpenSceneFlow/tree/main?tab=readme-ov-file#0-installation) to setup the environment.
 
+3. Run the training with the following command (modify the data path accordingly):
+```bash
+python train.py model=deltaflow batch_size=4 num_frames=5 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 train_data=${demo_train_data_path} val_data=${demo_val_data_path}
+```
+### Evaluation
+
+I will provide the pre-trained weights soon. Then you can run the evaluation with the following command:
+```bash
+python eval.py checkpoint=${path_to_pretrained_weights} dataset_path=${demo_data_path}
+```
+
+### Visualization
+Please refer to the [OpenSceneFlow](https://github.com/KTH-RPL/OpenSceneFlow/tree/main?tab=readme-ov-file#4-visualization) for visualization instructions.
+
+
+
+
+## Cite & Acknowledgements
 ```
 @article{zhang2025deltaflow,
     title={{DeltaFlow}: An Efficient Multi-frame Scene Flow Estimation Method},
@@ -36,5 +58,4 @@ Stay tuned and feel free to star ⭐ this repository to get notified when we pub
     journal={arXiv preprint arXiv:2508.17054},
 }
 ```
-
 This work was partially supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation and Prosense (2020-02963) funded by Vinnova. 

conf/config.yaml

@@ -28,7 +28,7 @@ gradient_clip_val: 5.0
 # optimizer ==> Adam
 optimizer:
   name: Adam # [Adam, AdamW]
-  lr: 1e-3
+  lr: 2e-4
 loss_fn: deflowLoss # choices: [ff3dLoss, zeroflowLoss, deflowLoss, seflowLoss]
 # add_seloss: {chamfer_dis: 1.0, static_flow_loss: 1.0, dynamic_chamfer_dis: 1.0, cluster_based_pc0pc1: 1.0}
 # ssl_label:

conf/model/deltaflow.yaml

@@ -0,0 +1,15 @@
+name: deltaflow
+
+target:
+  _target_: src.models.DeltaFlow
+  voxel_size: ${voxel_size}
+  point_cloud_range: ${point_cloud_range}
+  num_frames: ${num_frames}
+  planes: [16, 32, 64, 128, 256, 256, 128, 64, 32, 16] # 1st is #input channel, last is #output channel
+  # num_layer: [1, 1, 1, 1, 1, 1, 1, 1, 1] # the smallest model
+  num_layer: [2, 2, 2, 2, 2, 2, 2, 2, 2] # MinkUnet 18
+  # num_layer: [2, 3, 4, 6, 2, 2, 2, 2, 2] # MinkUnet 34
+  decay_factor: 0.4
+  decoder_option: default # choices: [default, deflow]
+
+val_monitor: val/Dynamic/Mean