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.gitattributes

@@ -58,3 +58,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
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+routes-xml/town12.xml filter=lfs diff=lfs merge=lfs -text

README.md

@@ -40,13 +40,67 @@ SynFlow: Scaling Up LiDAR Scene Flow Estimation with Synthetic Data
 [![arXiv](https://img.shields.io/badge/arXiv-2604.09411-b31b1b?logo=arxiv&logoColor=white)](https://arxiv.org/abs/2604.09411)
 [![page](https://img.shields.io/badge/Project-Page-green)](https://kin-zhang.github.io/SynFlow)
 
-Synthetic LiDAR scene-flow dataset collected from the [CARLA](https://carla.org/) , formatted for direct use with [OpenSceneFlow](https://github.com/KTH-RPL/OpenSceneFlow).
+The SynFlow dataset is a synthetic LiDAR scene flow benchmark collected using the [CARLA](https://carla.org/) simulator, and is formatted for seamless compatibility with [OpenSceneFlow](https://github.com/KTH-RPL/OpenSceneFlow). 
+We provide both the full dataset and [two pretrained model checkpoints](model-ckpt/) (one trained on the SynFlow-4k dataset, and another on SynFlow-4k augmented with real-world data; both are trained with [*DeltaFlow* backbone](https://github.com/Kin-Zhang/DeltaFlow)) to support further research and development by the community. 
+
+<p align="center">
+  <img alt="synflow_cover" src="https://kin-zhang.github.io/SynFlow/assets/images/cover.png" />
+</p>
 
 ## Dataset Summary
 
-SynFlow dataset provides dense 3D scene-flow ground truth for autonomous-driving research. An ego vehicle drives along pre-defined routes in multiple CARLA towns while a semantic LiDAR captures point clouds at **10 Hz**. Each **scene** is stored as a separate HDF5 file and spans roughly **20 seconds** (~200 frames for 64-beam LiDAR, ~30 s / 302 frames for 32-beam LiDAR). Dynamic objects (vehicles, pedestrians, cyclists, etc.) receive instance-level rigid-body flow labels; static background points receive ego-motion-compensated flow.
+SynFlow dataset provides dense 3D scene-flow ground truth for autonomous-driving research. 
+An ego vehicle drives along pre-defined routes in multiple CARLA towns while a LiDAR captures point clouds at **10 Hz**. 
+Each **scene** is stored as a separate HDF5 file and spans roughly **20 seconds** (~200 frames for 64-beam LiDAR, ~30 s / 302 frames for 32-beam LiDAR). 
+Dynamic objects (vehicles, pedestrians, cyclists, etc.) receive instance-level rigid-body flow labels; static background points receive ego-motion-compensated flow.
+
+**Folder Structure**
+
+```
+town-data-folder/
+├── scene-{town}{channels}{route_id:04d}{split:02d}.h5   # one file per scene
+├── ...
+├── index_total.pkl                     # frame index for training / visualization
+...
+
+
+# The backbone here is *DeltaFlow*
+model-ckpt/
+├── synflow-4k-longadp.ckpt # the ckpt trained only on our SynFlow-4k dataset
+├── synflow-real-longadp.ckpt # the ckpt trained on our SynFlow-4k dataset and real-world dataset (Av2, Waymo, nuscene)
+
+routes-xml/ # generate from our code, check: https://github.com/Kin-Zhang/SynFlow
+├── town01.xml
+├── town02.xml
+├── ...
+└── town12.xml
+```
+
+**Data Split**
+
+Here is the data split presented in our SynFlow paper Tab. 1, with the folder name and composition.
+
+| Split | # Annotated Frames | Folder Name | Composition |
+|:---:|:---:|:---:|:---:|
+| 1k | 271, 148 | `town-06-07-10` | Town06, 07, 10 arterials, complex junctions, rural roads |
+| 2k | 449,407 | `town-01-05` | Town01–05 roundabouts, multi-lane intersections |
+| 3k | 720,555 | `town-06-07-10` + `town-01-05` | Town06-07-10, Town01-05 |
+| 4k | 939,083 | `town-01-05` + `town-12` | Town01-05, Town12 |
+
+**File Naming Convention**
+
+```
+scene-{town_id}{channels}{route_id:04d}{scene_split:02d}.h5
+```
+
+| Field | Description | Example |
+|---|---|---|
+| `town_id` | CARLA map ID (digits only) | `01`, `12` |
+| `channels` | LiDAR beam count | `32`, `64` |
+| `route_id` | 4-digit route index within the town | `0042` |
+| `scene_split` | 2-digit split index when a long route is divided | `00`, `01`, … |
 
-Data is organized per scene, following the same HDF5 layout used by OpenSceneFlow for Argoverse 2, Waymo, and nuScenes derivatives.
+**Example:** `scene-0164004200.h5` → Town01, 64-beam LiDAR, route 42, split 0. Scenes with fewer than 120 valid frames are discarded during collection.
 
 ## Data Collection
 
@@ -71,31 +125,7 @@ The data collection code can be found in [SynFlow](https://github.com/Kin-Zhang/
 
 Default: LiDAR is mounted at `z = 2.1 m` above the ego vehicle origin.
 
-## File Structure
-
-```
-data/
-├── scene-{town}{channels}{route_id:04d}{split:02d}.h5   # one file per scene
-├── index_total.pkl                                         # frame index for training / visualization
-└── index_eval.pkl                                          # evaluation subset (optional)
-```
-
-### File Naming Convention
 
-```
-scene-{town_id}{channels}{route_id:04d}{scene_split:02d}.h5
-```
-
-| Field | Description | Example |
-|---|---|---|
-| `town_id` | CARLA map ID (digits only) | `01`, `12` |
-| `channels` | LiDAR beam count | `32`, `64` |
-| `route_id` | 4-digit route index within the town | `0042` |
-| `scene_split` | 2-digit split index when a long route is divided | `00`, `01`, … |
-
-**Example:** `scene-0164004200.h5` → Town01, 64-beam LiDAR, route 42, split 0.
-
-Scenes with fewer than 120 valid frames are discarded during collection.
 
 ## HDF5 Schema
 
@@ -129,7 +159,9 @@ Each HDF5 file contains one scene. Every frame is stored as an HDF5 **group** ke
 
 `ground_mask` is `True` for points whose CARLA semantic tag is one of `{Road, SideWalk, Terrain, RoadLine, Ground}` (tags 1, 2, 10, 24, 25).
 
-## Index Files
+## Quick Usage Example
+
+**Index Files**
 
 `index_total.pkl` is a pickled list of `[scene_id, timestamp]` pairs covering all frames, required for OpenSceneFlow training and visualization:
 
@@ -141,7 +173,7 @@ with open("index_total.pkl", "rb") as f:
 
 `index_eval.pkl` (optional) subsamples every 5 frames with a minimum non-ground point count, for standardized evaluation.
 
-## Usage Example
+**HDF5 File Example**
 
 ```python
 import h5py
@@ -169,6 +201,17 @@ For visualization and training, use the [OpenSceneFlow](https://github.com/KTH-R
 ```bash
 python tools/visualization.py --res_name flow --data_dir /path/to/data
 ```
+
+For training, please follow the [OpenSceneFlow](https://github.com/KTH-RPL/OpenSceneFlow) to setup environment and change the data path to this dataset, example command:
+```bash
+python train.py slurm_id=$SLURM_JOB_ID wandb_mode=online wandb_project_name=synflow \
+     train_data="['data/town-06-07-10', 'SynFlow/data/town-01-05', 'SynFlow/data/town-12']" \
+     val_data='$DATA_DIR/val' model=deltaflow loss_fn=deltaflowLoss model.target.decoder_option=default \
+     num_workers=16 num_frames=5 model.target.decay_factor=0.4 epochs=21 batch_size=2 \
+     save_top_model=3 val_every=3 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=StepLR +optimizer.scheduler.step_size=3 +optimizer.scheduler.gamma=0.9
+```
+
 <!-- 
 ## Coordinate System
 
@@ -208,4 +251,6 @@ If you use this dataset, please cite our papers and acknowledge the CARLA simula
 
 ## License
 
-Dataset is released under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/). CARLA itself is subject to its own [license terms](https://carla.org/).
+- **SynFlow-4k dataset** and checkpoints trained only on SynFlow-4k dataset are released under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/). 
+- CARLA itself is subject to its own [license terms](https://carla.org/).
+- Checkpoints trained on SynFlow-4k + real-world dataset are released under [Argoverse 2](https://www.argoverse.org/about.html#terms-of-use), [Waymo Open Dataset](https://waymo.com/open/terms/), [nuScenes](https://www.nuscenes.org/terms-of-use) licenses. Please refer to the respective licenses for more details.

routes-xml/town12.xml

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