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
- Frames are extracted from the rectified stereo pair; frame counts are asserted at every stage.
- 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). - Metric depth is delivered natively in each clip's
LeRobot_v3.0/dataset (mm on lerobot ≥ 0.6.0). The colorizedDepth.mp4is a preview only. - 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 mbefore 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.