docs/DATA_CARD.md

# Hub Stereo RGB-D Sample Delivery · Data Card

Hub Data. July 2026. Format contract: `README.md` (same folder).

## 1. What this is

An egocentric (head-mounted) stereo RGB-D corpus of a human performing everyday manipulation and
locomotion tasks in indoor and outdoor service environments, with dense machine-generated metric
depth, per-frame IMU, 6-DoF visual-inertial pose, and stereo calibration. This is a human
demonstration corpus; there are no robot actions, joint states, or teleoperation labels. It is
intended for representation, depth, and world-model pretraining, for stereo-depth and
egocentric-perception evaluation, and as egocentric human-manipulation demonstration data.

- **9 clips, 29,758 frames, ~12 minutes**, 1920×1200 per eye, mixed ~30 fps and 60 fps capture.

## 2. Per-clip contents

| # | Clip | Frames | Duration | What's demonstrated |
|---|---|---|---|---|
| 01 | Restaurant · Chop Vegetables | 1,800 | 60 s | Chopping vegetables: fast periodic knife/tool motion in the near field. |
| 02 | Restaurant · Arrange Table | 4,800 | 163 s | Setting a table: plates, cutlery, repeated reach-place cycles; longest clip. |
| 03 | Restaurant · Arrange Glasses | 1,920 | 66 s | Arranging drinking glasses on a counter: close-range bimanual manipulation of transparent objects (a classically hard stereo case). |
| 04 | Restaurant · Wash Dishes | 1,680 | 57 s | Washing dishes at a sink: running water, wet specular surfaces, frequent very-close-range activity (highest near-field saturation). |
| 05 | Meeting Room · Arrange Chairs | 3,627 | 60 s | Moving and arranging chairs: room-scale motion, large low-texture walls. 60 fps. |
| 06 | Office · Photocopy | 4,499 | 75 s | Operating a photocopier: device interaction, uniform surfaces. 60 fps. |
| 07 | Open Space · Rack the Ball | 3,967 | 66 s | Throwing and racking balls: the extreme-motion case (fast small objects; see §5). 60 fps. |
| 08 | Outdoor · Sweeping | 3,000 | 101 s | Sweeping an outdoor seating area: natural light, long-handle tool use, ground-plane geometry. |
| 09 | Pantry · Wash Tea Set | 4,465 | 74 s | Washing a tea set: small-object manipulation at a sink. 60 fps; best temporal stability in the set. |

Total: **29,758 frames** across the 9 clips.

## 3. Collection hardware

- Head-mounted global-shutter stereo camera (StereoLabs ZED X Mini): 1920×1200 per eye,
  **49.88 mm baseline**, factory-rectified (D=0, R=I). Four clips captured at 60 fps, the rest
  at ~29-30 fps.
- Built-in IMU, delivered frame-locked to the recording (so ~30-60 Hz here, i.e. at the video
  rate, not the sensor's raw high rate); on-device 6-DoF visual-inertial poses.
- Delivered per clip as a Foxglove `Data.mcap` (stereo/mono RGB, IMU, VIO pose, camera calibration,
  static transforms), the side-by-side stereo MP4, a mono MP4, a colorized Hub metric-depth preview,
  and a self-contained LeRobot v3.0 dataset (`LeRobot_v3.0/`).

## 4. Depth provenance

1. Frames are extracted from the rectified stereo pair; frame counts are asserted at every stage.
2. Dense per-pixel depth is computed from the stereo pair using Hub's stereo depth model
   (July 2026 build), then temporally smoothed for frame-to-frame stability. Depth is metric via
   the calibrated conversion `Z = f · B / disparity` (f = 754.949 px rectified, B = 49.88 mm).
3. Metric depth is delivered natively in each clip's `LeRobot_v3.0/` dataset (mm on lerobot ≥ 0.6.0). The
   colorized `Depth.mp4` is a preview only.
4. The rectified calibration used for the depth conversion is cross-checked against the as-recorded
   sensor calibration; the two differ by at most 0.21 px in focal length and 0.0033 mm in baseline,
   confirming the rectification (full chain in each clip's `LeRobot_v3.0/meta/calibration.json`).

## 5. Accuracy and limitations (honest)

- **Coverage.** Depth is dense: every clip retains **≥ 99.996% valid pixels**, and unresolved
  (code-0) pixels are a negligible fraction. Density is not the same as reliability: the near-field
  and transparent/specular regions below are dense but low-confidence.
- **Metric basis.** Depth is geometrically metric: it derives from the validated stereo
  calibration and the disparity solve. In a separate controlled bench (a fiducial target of known
  geometry), the same depth model measured ~0.93% RMS error. That figure is a **bench reference,
  not an on-clip measurement**: the delivered clips have no fiducial ground truth, so on-clip
  metric accuracy is **not independently validated**. Treat depth as metrically-scaled and
  geometrically consistent, and validate against your own reference if you need a certified number.
- **Far-range precision.** This is a short 49.88 mm baseline, so metric precision degrades with
  range, roughly quadratically (about one disparity-pixel ≈ 8% depth error near 3 m, and coarser
  toward the 10 m clip). Near and table-scale depth is the strong regime; treat room-scale and
  outdoor mid and far depth as scale-correct but low-precision.
- **Near field (< 0.25 m).** On close-range clips (02, 03, 04), roughly 10-16% of pixels fall
  inside the ~0.25 m near band, dominated by self-occlusion, the wearer's own body, and shrinking
  left/right stereo overlap rather than raw resolvability. They are reported at their computed
  values, not masked; treat them as low-confidence.
- **Invalid pixels.** Unresolved pixels (disparity < 1 px; rare) are encoded as code 0, which
  decodes to the 0.01 m (10 mm) floor rather than NaN. Drop `depth ≤ 0.01 m` before training or
  metric use.
- **Extreme motion (clip 07).** On the fastest-motion clip (thrown balls), brief whole-scene depth
  re-grades can appear on a small fraction of frames (~3%). Disclosed in the clip card.
- **Temporal variation (clip 03, Arrange Glasses).** This clip has greater capture-motion/depth
  temporal variation than clip 01 (Chop Vegetables). Sequential review found no alternating-frame,
  freeze, blank, or decode defect. The media is retained unchanged for temporal-variation evaluation.
- **Edge softening.** Temporal smoothing trades a small amount of edge sharpness at fast motion
  boundaries for frame-to-frame stability.
- **Transparent and specular** surfaces (glassware, wet surfaces) produce locally unreliable depth,
  as they do for any stereo method.

## 6. Confidentiality

Shipped artifacts carry no capture-partner identity, operator IDs, or environment IDs. The
recordings retain the camera unit's serial number and the native capture timestamps as ordinary
sensor provenance (as Aria-class and DROID-class releases do); these identify the physical camera
and the recording, not any partner or subject.

## 7. Terms

This sample is provided for evaluation. Contact Hub Data for data-use terms.