Upload README.md

README.md

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+---
+license: cc-by-nc-4.0
+task_categories:
+  - other
+tags:
+  - scene-flow
+  - lidar
+  - autonomous-driving
+  - carla
+  - synthetic
+  - 3d
+language:
+  - en
+configs:
+  - config_name: 1k
+    data_files:
+      - split: train
+        path: "train/town-06-07-10/*"
+  - config_name: 2k
+    data_files:
+      - split: train
+        path: "train/town-01-05/*"
+  - config_name: 3k
+    data_files:
+      - split: train
+        path:
+          - "train/town-06-07-10/*"
+          - "train/town-01-05/*"
+  - config_name: 4k
+    data_files:
+      - split: train
+        path:
+          - "train/town-01-05/*"
+          - "train/town-12/*"
+---
+
+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).
+
+## 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.
+
+Data is organized per scene, following the same HDF5 layout used by OpenSceneFlow for Argoverse 2, Waymo, and nuScenes derivatives.
+
+## Data Collection
+
+The data collection code can be found in [SynFlow](https://github.com/Kin-Zhang/SynFlow) github repository.
+
+| 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.
+
+## 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
+
+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).
+
+## Index Files
+
+`index_total.pkl` is a pickled list of `[scene_id, timestamp]` pairs covering all frames, required for OpenSceneFlow training and visualization:
+
+```python
+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.
+
+## Usage Example
+
+```python
+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](https://github.com/KTH-RPL/OpenSceneFlow) toolchain:
+
+```bash
+python tools/visualization.py --res_name flow --data_dir /path/to/data
+```
+<!-- 
+## Coordinate System
+
+CARLA uses a left-handed coordinate system (LHS). All stored data is converted to a standard **right-handed system (RHS)** by flipping the Y axis on point coordinates, flow vectors, and the ego pose matrix. Point clouds and flow are expressed in the **LiDAR sensor frame** at frame *t*; `pose` gives the ego-to-world transform at frame *t*. -->
+
+## Collection Code
+
+This dataset is generated by [OpenSF-CARLA](https://github.com/KTH-RPL/opensf-carla). See the repository for route generation, multi-instance collection (`run_all.py`), and configuration details.
+
+## Citation
+
+If you use this dataset, please cite our papers and acknowledge the CARLA simulator:
+
+```bibtex
+@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}
+}
+@inproceedings{dosovitskiy2017carla,
+  title={CARLA: An open urban driving simulator},
+  author={Dosovitskiy, Alexey and Ros, German and Codevilla, Felipe and Lopez, Antonio and Koltun, Vladlen},
+  booktitle={Conference on robot learning},
+  pages={1--16},
+  year={2017},
+  organization={PMLR}
+}
+```
+
+## 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/).