Datasets:

Cloth-splatters
/

DEDO_demos

license: other
task_categories:
  - other
tags:
  - cloth
  - deformable-objects
  - state-estimation
  - point-cloud
  - simulation
  - dedo
pretty_name: DEDO GSE Demos
size_categories:
  - 100K<n<1M

DEDO GSE Demos (`gse_demos_v1.h5`)

Cloth-manipulation demonstrations collected in the DEDO (Dynamic Environments with Deformable Objects) simulator, used as an out-of-distribution evaluation set for graph-based cloth state estimation (GSE).

Each demo is a single trajectory of a deforming cloth, recorded with synchronized ground-truth mesh state, a single-camera depth point cloud, and an RGB image per timestep.

Split	Cloths	Trajectories	Steps per traj.	Vertices
`training`	181	1 per cloth	44–121	209–221
`validation`	21	1 per cloth	58–121	209–221

File structure

HDF5 layout (gse_demos_v1.h5, ~1.5 GB):

<split>/                              # "training" or "validation"
  cloth_XXX/
    rest_positions   (V, 3)  float32  # canonical / rest mesh, constant per cloth
    faces            (F, 3)  int64    # triangle topology (F = 368 for V = 219)
    trajectory_0/
      step_YYYY/
        positions              (V, 3)        float32  # deformed mesh state at t
        pointclouds/cam_0      (512, 3)       float32  # single-camera depth back-projection
        rgb                    (128, 128, 3) uint8     # RGB render at t

Vertex count V varies per cloth (209–221); topology (faces) is fixed within a cloth.

Coordinate & scale conventions

⚠️ This dataset does not share the frame or scale of the fold_unfold_lift cloth training data. Pre-process before feeding it to state-estimation models.

Axis convention — GSE cloths lie flat in the XZ plane with gravity along +Y. The fold_unfold_lift training cloths lie flat in XY with gravity along +Z. Map GSE → training with a +90° rotation about X: (x, y, z) -> (x, -z, y).
Scale — GSE cloths are ~3–6 world units wide; training cloths are ~12 cm. Scale each GSE cloth so its rest-mesh max extent ≈ 0.1225.
Mesh density — GSE meshes have 209–221 vertices vs. training's 44–151, and point clouds are single-camera (cam_0, ~512 points). Both are out-of-distribution for models trained on the multi-camera fixed-template data.

Without the rotation fix, Chamfer error on the GPS state-est model was ~982 mm; with it, ~300–780 mm (a real residual domain gap).

Usage

import h5py

with h5py.File("gse_demos_v1.h5", "r") as f:
    grp = f["training"]["cloth_000"]
    rest = grp["rest_positions"][:]          # (V, 3)
    faces = grp["faces"][:]                  # (F, 3)
    traj = grp["trajectory_0"]
    step = traj["step_0000"]
    positions = step["positions"][:]         # (V, 3) deformed mesh
    pcd = step["pointclouds"]["cam_0"][:]    # (512, 3) point cloud
    rgb = step["rgb"][:]                     # (128, 128, 3)

An interactive viewer (visualize_gse_demos.py) accompanies the dataset; it shows the RGB image, deformed mesh, point cloud, and canonical mesh side-by-side with a timestep slider and per-demo selector.

DEDO GSE Demos (gse_demos_v1.h5)

Contents

File structure

Coordinate & scale conventions

Usage

DEDO GSE Demos (`gse_demos_v1.h5`)