KTH/SynFlow

SynFlow: Scaling Up LiDAR Scene Flow Estimation with Synthetic Data

The SynFlow dataset is a synthetic LiDAR scene flow benchmark collected using the CARLA simulator, and is formatted for seamless compatibility with OpenSceneFlow. We provide both the full dataset and two pretrained model checkpoints (one trained on the SynFlow-4k dataset, and another on SynFlow-4k augmented with real-world data; both are trained with DeltaFlow backbone) to support further research and development by the community. Check licenses for more detail usage.

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 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
├── ...
└── 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	Storage Size (GB)
1k	271, 148	town-06-07-10	Town06, 07, 10 arterials, complex junctions, rural roads	214G
2k	449,407	town-01-05	Town01–05 roundabouts, multi-lane intersections	419G
3k	720,555	town-06-07-10 + town-01-05	Town06-07-10, Town01-05	633G
4k	939,083	town-01-05 + town-12	Town01-05, Town12	986G

Command for Download

# full
hf download KTH/SynFlow --repo-type dataset --local-dir ./SynFlow-data

# 1k split (for a quick training test) around 214G
hf download KTH/SynFlow --repo-type dataset --include "town-06-07-10/*" --local-dir ./SynFlow-data/town-06-07-10

File Naming Convention: scene-{town_id}{channels}{route_id:04d}{scene_split:02d}.h5,
where town_id is the CARLA town number (e.g. 01, 12),
channels is LiDAR beam count (32, 64),
route_id is a 4-digit route index (e.g. 0042),
and scene_split is a 2-digit split for long routes (00, 01, etc). 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

This dataset is generated by SynFlow-Github. See the repository for route generation, multi-instance collection, and configuration details.

Property	Value
Simulator	CARLA 0.9.16
Sensor	sensor.lidar.ray_cast_semantic
Frame rate	10 Hz (fixed_delta_seconds = 0.1)
Coordinate system	Right-handed (RHS); Y-axis flipped from CARLA's native left-handed system
Ego vehicle	Tesla Model 3 with BehaviorAgent route following
NPCs	~70 vehicles + ~80 pedestrians per scene (town-dependent)
Towns	Town01–Town10, Town12 (diverse urban, highway, rural, and large-map environments)

LiDAR Configurations

Channels	Range	FOV (upper / lower)	Points/sec	Frames / scene	Duration
64	85 m	+10° / −30°	460,000	201	~20 s
32	75 m	+10° / −30°	160,000 (typical)	302	~30 s

Default: LiDAR is mounted at z = 2.1 m above the ego vehicle origin.

HDF5 Schema

Each HDF5 file contains one scene. Every frame is stored as an HDF5 group keyed by its simulation timestamp in microseconds (e.g. "1577836800000000"). There is no separate timestamps dataset—the group key is the timestamp.

Key	Shape	Dtype	Description
lidar	(N, 3)	float32	Point cloud in sensor frame (X, Y, Z), RHS
pose	(4, 4)	float64	Ego vehicle 4×4 transformation matrix (world ← ego), RHS
flow	(N, 3)	float32	Scene-flow vector from frame t to t+1 in sensor frame, RHS
flow_is_valid	(N,)	bool	Per-point flow validity mask (currently all True)
flow_category_indices	(N,)	uint8	Semantic category index; 0 = background/static
flow_instance_id	(N,)	int16	Instance ID; -1 = background, positive = dynamic object
ground_mask	(N,)	bool	True for ground points (road, sidewalk, terrain, road line, ground)

N varies per frame depending on LiDAR density and scene complexity.

Scene-Flow Ground Truth

• Background (static) points: flow computed by projecting world coordinates through the ego motion from frame t to t+1 (sensor frame).
• Foreground (dynamic) points: flow refined using per-object rigid-body transforms derived from NPC bounding boxes and semantic instance tags.
• Points inside the ego vehicle bounding box are filtered before saving.
• The first frame of the first scene in a collection run is skipped (no t−1 reference for flow).

Instance & Category Labels

• flow_instance_id: dynamic objects are labeled with npc.id % 32000 + 1; background is -1 (training code uses 0-indexed instances, so background must not be 0).
• flow_category_indices: maps to the AnnotationCategories enum used in OpenSceneFlow (e.g. REGULAR_VEHICLE, PEDESTRIAN, TRUCK, BICYCLE, MOTORCYCLE, BUS, …). Index 0 denotes background / static.

Ground Mask

ground_mask is True for points whose CARLA semantic tag is one of {Road, SideWalk, Terrain, RoadLine, Ground} (tags 1, 2, 10, 24, 25).

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:

import pickle
with open("index_total.pkl", "rb") as f:
    index = pickle.load(f)  # e.g. [["scene-0164004200", "1577836800000000"], ...]

index_eval.pkl (optional) subsamples every 5 frames with a minimum non-ground point count, for standardized evaluation.

HDF5 File Example

import h5py
import numpy as np

scene_id = "scene-0164004200"
timestamp = "1577836800000000"

with h5py.File(f"{scene_id}.h5", "r") as f:
    frame = f[timestamp]
    points  = frame["lidar"][:]           # (N, 3) float32
    pose    = frame["pose"][:]             # (4, 4) float64
    flow    = frame["flow"][:]             # (N, 3) float32
    valid   = frame["flow_is_valid"][:]    # (N,) bool
    cats    = frame["flow_category_indices"][:]  # (N,) uint8
    inst    = frame["flow_instance_id"][:]       # (N,) int16
    ground  = frame["ground_mask"][:]      # (N,) bool

# Dynamic (non-background) points
dynamic_mask = inst != -1

For visualization and training, use the OpenSceneFlow toolchain:

python tools/visualization.py --res_name flow --data_dir /path/to/data

For training, please follow the OpenSceneFlow to setup environment and change the data path to this dataset, example command:

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

Citation

If you use this dataset, please cite our papers (datasets and models), more works on OpenSceneFlow.

@article{zhang2026synflow,
  author    = {Zhang, Qingwen and Zhu, Xiaomeng and Jiang, Chenhan and Jensfelt, Patric},
  title     = {{SynFlow}: Scaling Up LiDAR Scene Flow Estimation with Synthetic Data},
  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}
}

License

• SynFlow-4k dataset and checkpoints trained only on SynFlow-4k (e.g. synflow-4k-longadp.ckpt) are released under CC BY 4.0. You are free to share, adapt, and use them for any purpose, including commercial use, as long as you provide appropriate credit (see Citation).
• This dataset was generated using the CARLA simulator. CARLA code is distributed under the MIT License; CARLA digital assets (maps, buildings, vehicles, etc.) are distributed under CC BY.
• Checkpoints trained on SynFlow-4k plus real-world datasets (e.g. synflow-real-longadp.ckpt) remain subject to Argoverse 2, Waymo Open Dataset, and nuScenes licenses. Note that checkpoints trained on real-world datasets are not available for commercial use because of the restrictions of the real-world datasets. Please refer to the respective licenses for more details.