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README.md

@@ -1,28 +1,123 @@
+---
+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 the processed HDF5 dataset and demo videos for the Kinder-worldmodel project.
+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`  
-  The processed HDF5 file containing complete point cloud data and point tracking information.
+* `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 the complete point cloud visualization.
+* `videos/complete_pointcloud_demo.mp4`
+  Demo video showing complete point cloud visualization.
 
-- `videos/pointcloud_tracking_demo.mp4`  
+* `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 HDF5 file stores point cloud data generated from the processing pipeline.  
-It is intended for inspecting complete point clouds and demonstrating point tracking results.
+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 dataset directly from this Hugging Face repository.
+You can download the files directly from this Hugging Face dataset repository.
 
-You can also download it using Python:
+You can also download a file using Python:
 
 ```python
 from huggingface_hub import hf_hub_download
@@ -34,3 +129,65 @@ file_path = hf_hub_download(
 )
 
 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