Physical AI Annotation Sample — Multi-Sensor Scenes
A sample of our Physical AI annotation service output: 3D boxes, point-cloud segmentation, and 6-DoF pose on multi-sensor scenes — a showcase of annotation capability, not a research release.
- Rights-cleared
- Consent documented
- 🇮🇳 India data residency
- Languages
- Labels in English
- Scale
- 1,200 annotated frames · 1200 records
- Inter-annotator agreement
- α = 0.91
- QA pass rate
- 99.40%
Measured, audited, reproducible
- IAA / Krippendorff α
- 0.91
- QA pass rate
- 99.40%
3D box and segmentation labels are produced under a multi-pass QA pipeline with geometric cross-checks, reaching α = 0.91 and a 99.4% QA pass rate. Each frame is verified for temporal consistency across the sequence.
This is a representative service output sample demonstrating our annotation capability on multi-sensor scenes — including the annotation of physical and sensor data captured by external collection programmes. Sensor inputs in this sample are synthetic / internally captured and rights-cleared for showcase use; all annotation work is performed within India.
Our annotation teams label RGB-D, LIDAR, and IMU-aligned frames with 3D bounding boxes, point-cloud semantic segmentation, 6-DoF object pose, and frame-accurate event markers. Every sequence passes geometric validation, temporal-consistency review, and a senior QA audit before delivery. This is an annotation service capability — engage us to annotate your own collected physical data.
What's inside each record
A representative schema for Physical AI Annotation Sample — Multi-Sensor Scenes. The full data card ships the complete field dictionary, value ranges and annotation rubric.
| Field | Type | Example |
|---|---|---|
| scene_id | string (uuid) | "scn_0f8a…" |
| sensors | array<enum> | ["lidar","rgb","imu"] |
| frame_path | string | "frames/scn_0f8a/…" |
| annotations | array<object> | [{label, bbox3d, track_id}] |
| events | array<enum> | ["pedestrian_cross"] |
| language | string (ISO 639) | "lab" |
| annotator_id | string (hashed) | "anr_7f3…" |
| qa_status | enum | "passed" |
| consent_ref | string | "cns_2024_…" |
Representative schema — exact fields and value ranges are documented in the dataset card shipped with every licence.
License tiers
Choose the tier that matches your use case. Every tier ships with the full data card, provenance log, and quality report.
Evaluation Sample
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Request the sample to evaluate our Physical AI annotation quality and label schema.
Request the sampleAnnotation Service Engagement
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Engage our team to annotate your collected multi-sensor data to spec — 3D boxes, segmentation, pose, and events.
Scope a projectEnterprise / Sovereign
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