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license: mit
task_categories:
- robotics
- computer-vision
tags:
- point-cloud
- hdf5
- point-tracking
- canonical-point-cloud
- rigid-transform
- world-model
pretty_name: Kinder Worldmodel Dataset
---
# Kinder-worldmodel Dataset
This repository contains processed HDF5 datasets and demo videos for the Kinder-worldmodel project.
The uploaded files demonstrate two related point cloud representations:
1. **Tracked / all-point-cloud HDF5 data**
2. **Canonical point cloud replay using per-geom rigid transforms**
## Files
* `sweep_tracked_pointcloud_all.hdf5`
Processed HDF5 file containing complete point cloud data and point tracking information.
* `sweep_canonical.hdf5`
HDF5 file using a transform-based representation. Instead of storing per-timestep point clouds, it stores canonical surface points for each rigid geom and per-timestep 4x4 transforms.
* `videos/complete_pointcloud_demo.mp4`
Demo video showing complete point cloud visualization.
* `videos/point_tracking_demo.mp4`
Demo video showing point tracking across frames.
* `videos/canonicalpointcloud-excluding kitchen floor.mp4`
Demo video showing transform-based canonical point cloud replay at 30 fps.
## Dataset Description
The dataset is intended for inspecting complete point clouds, point tracking, and transform-based point cloud replay.
For the canonical point cloud representation, each rigid geom is stored using:
* one canonical surface point set in local coordinates
* per-timestep 4x4 rigid transforms
This avoids storing a full point cloud for every timestep.
## Canonical Point Cloud Replay
The canonical point cloud replay demo is rendered from the new HDF5 format, not from per-timestep point clouds stored in the file.
For each rigid geom, the file stores:
```text
canonical_pointcloud/<geom_name>/xyz
geom_transforms/<geom_name>[t]
```
At each frame, world-space points are reconstructed using:
```python
world_pts = (T @ pts_h.T).T[:, :3]
```
where:
* `pts_h` is the homogeneous version of the canonical local point cloud
* `T` is the 4x4 rigid transform for that geom at timestep `t`
* `world_pts` are the reconstructed world-frame points
### What is shown in the canonical replay video
The clip shows:
* reconstructed scene at 30 fps from `hdf5_data/sweep_canonical.hdf5`
* task: `SweepIntoDrawer3D-o5`
* number of demos: 1
* kitchen and floor geometry filtered out
* robot, task objects, and other non-kitchen geometry kept
The filtered-out geoms include names containing:
```text
kitchen
floor
```
This removes cabinets, panels, drawers, and floor geometry.
The displayed result is a filtered view of the same transform-based representation. It shows that geom names can be used to drop background geometry and focus on the manipulator and task-relevant parts without re-exporting the dataset.
One-line summary:
```text
Transform-based point cloud replay at 30 fps; kitchen/floor removed by geom-name filter.
```
## Related Fields
The same canonical HDF5 file also contains fields that are not visualized in the canonical replay video:
* `actions`
Original demo actions.
* `actions_delta_ee_transform`
End-effector delta transform per action step, computed at `robot_pinch_site`.
* `obs/ee_pose`
End-effector pose per step for debugging.
## How to Download
You can download the files directly from this Hugging Face dataset repository.
You can also download a file using Python:
```python
from huggingface_hub import hf_hub_download
file_path = hf_hub_download(
repo_id="Flashkernel/Kinder-worldmodel",
filename="sweep_tracked_pointcloud_all.hdf5",
repo_type="dataset"
)
print(file_path)
```
To download the canonical HDF5 file:
```python
from huggingface_hub import hf_hub_download
file_path = hf_hub_download(
repo_id="Flashkernel/Kinder-worldmodel",
filename="sweep_canonical.hdf5",
repo_type="dataset"
)
print(file_path)
```
## How to Inspect the HDF5 File
Install dependencies:
```bash
pip install h5py
```
Then inspect the file structure:
```python
import h5py
file_path = "sweep_canonical.hdf5"
with h5py.File(file_path, "r") as f:
def print_structure(name, obj):
if isinstance(obj, h5py.Dataset):
print(name, obj.shape, obj.dtype)
else:
print(name)
f.visititems(print_structure)
```
## Visualization
The demo videos show:
1. Complete point cloud visualization
2. Point tracking across frames
3. Canonical point cloud replay from canonical points and per-geom 4x4 transforms
The canonical replay video is generated from canonical local point sets and rigid transforms. It does not require storing a dense per-frame point cloud in the HDF5 file.
## Usage Notes
This is a dataset repository, so it is not meant to be run directly.
To use the data, download the HDF5 file and load it with `h5py`. The videos provide visual examples of the stored representations and reconstruction results.
## Repository Link
Dataset page:
https://huggingface.co/datasets/Flashkernel/Kinder-worldmodel
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