Datasets:

Flashkernel
/

Kinder-worldmodel

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:

Tracked / all-point-cloud HDF5 data
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:

canonical_pointcloud/<geom_name>/xyz
geom_transforms/<geom_name>[t]

At each frame, world-space points are reconstructed using:

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:

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:

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:

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:

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:

pip install h5py

Then inspect the file structure:

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:

Complete point cloud visualization
Point tracking across frames
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