AUTOMOTIVE / ROBOTICS / CONSTRUCTION / SURVEYING

3D point cloud annotation,shipped fusion-ready.

3D cuboid, 9-DoF tracking, semantic per-point, sensor-fusion alignment for LiDAR + camera + radar. EEA-resident, kappa-gated, calibration-verified.

  • 9-DoF cuboid + tracking
  • EEA-resident
Scope a Data Project See sample annotations
vehicle pedestrian cyclist ego
FORMAT KITTI + nuScenes JSON
VIEW BIRD'S EYE

Schematic. Production runs against your raw scans, multi-frame, calibration-verified.

PROCUREMENT READINESS

Compliance posture for 3D perception training data.

Article 10 enforcement begins 2 August 2026. ISO 21448 SOTIF data-governance overlays automotive engagements.

Per-delivery artefact pack

Annotation guideline (versioned)
Calibration verification log
Per-class 3D IoU + BEV mAP report
Tracking continuity report
Records of processing (Article 30)
Signed DPA + sub-processor list

EU AI Act Article 10

Data governance for high-risk AI in vehicles. Annotation provenance, bias-examination notes, ODD coverage documentation.

GDPR Articles 7, 28, 30

Per-contributor consent. Processor agreement. Records of processing. 30-day erasure SLA.

ISO 21448 + UN R157 aligned

SOTIF data-governance aligned. ALKS edge-case taxonomy. EEA-resident processing outside US CLOUD Act reach.

Request a Procurement Readiness Brief →

We map the evidence package to your data, risk class, and deployment environment.

PRIMITIVE TYPES

Five 3D annotation primitives.

Which one you procure depends on what your perception stack needs. The double-spend failure mode is ordering cuboid when you needed per-point semantic.

7-DoF box + class

3D cuboid

7-DoF bounding box (x, y, z, w, h, l, yaw) plus class. Sufficient for most AD object-detection baselines and counting tasks.

Schema
7 floats: x,y,z,w,h,l,yaw + class
Gate
3D IoU >= 0.6 vehicle, >= 0.5 pedestrian
Use case
AD/ADAS perception baseline, counting, basic tracking
x/y/z m w/h/l m yaw rad
Compatible
  • KITTI
  • nuScenes
  • Waymo
yaw, pitch, roll added

9-DoF cuboid

Adds pitch + roll to the 7-DoF box. Captures orientation for rare poses, robotics grasp planning, and motorcycles or off-road cases.

Schema
9 floats: x,y,z,w,h,l,yaw,pitch,roll
Gate
3D IoU >= 0.6 + orientation err < 5deg
Use case
Orientation-aware tracking, grasp planning, edge-case poses
x/y/z m w/h/l m y/p/r rad
Compatible
  • nuScenes
  • A2D2
every point labelled

Semantic per-point

Every LiDAR return labelled with a class. Per-point granularity for road surface, vegetation, drivable area, terrain modelling.

Schema
uint8 per point, N classes
Gate
mIoU >= 0.65 over taxonomy
Use case
Road surface, drivable area, terrain DEM
points N classes N
Compatible
  • SemanticKITTI
  • nuScenes-LiDARseg
semantic + instance

Panoptic 3D

Unified per-point class plus per-instance IDs. Single representation for full scene understanding (stuff and things).

Schema
class + instance_id per point
Gate
PQ >= 0.55, RQ >= 0.7
Use case
AD stack training, full scene perception
class id instance id
Compatible
  • Panoptic-nuScenes
  • SemanticKITTI
frame-to-frame continuity

Tracking IDs

Persistent instance IDs across frames. Enables multi-frame perception, prediction, and planning. Identity-switch rate reported in delivery.

Schema
instance_id stable across t
Gate
ID-switches < 2% / km
Use case
Prediction, planning, multi-frame perception
instance id t frame
Compatible
  • nuScenes
  • Waymo
  • KITTI tracking
WHERE LIDAR IS PROCURED

Six verticals with quantitative anchors.

Different sensors, different ontologies, same delivery contract: EEA-resident annotation, calibration verified, Article 30 records on delivery.

  • Automotive AD / ADAS

    Cuboid + tracking + multi-modal fusion for perception stacks.

    Customer class
    Tier-1 OEM perception team
    Throughput
    15k-50k scans / week
    Primary primitive
    9-DoF cuboid + tracking

  • Mobile robotics + AMR

    Warehouse SLAM, delivery autonomy, grasp planning.

    Customer class
    Tier-2 robotics integrator
    Throughput
    5k-20k scans / week
    Primary primitive
    cuboid + grasp regions

  • Construction + surveying

    BIM-ready 3D, structural surveying, terrain DEM.

    Customer class
    AEC firm or large surveyor
    Throughput
    ~100M points / project
    Primary primitive
    semantic per-point + LAS/LAZ

  • Agriculture + drone

    Canopy height, biomass, per-tree segmentation, yield.

    Customer class
    AgriTech platform vendor
    Throughput
    seasonal: ~10k drone scans
    Primary primitive
    semantic per-point

  • Infrastructure monitoring

    Rail, pipeline, power line. Change detection, defect localization.

    Customer class
    Asset-operator inspection team
    Throughput
    periodic: km-scale scans
    Primary primitive
    panoptic 3D + change-diff

  • Geospatial mapping

    High-density urban + rural maps for HD positioning.

    Customer class
    Mapping / GIS vendor
    Throughput
    ~50k frames / month
    Primary primitive
    semantic per-point

HOW WE LABEL

Every project clears the same six gates.

Calibration verified BEFORE annotation. 3D IoU thresholds documented. Multi-frame consistency checked.

01

Schema + calibration verification

Class taxonomy locked with your team. Calibration data (intrinsic, extrinsic, PTP-sync timestamps) verified before annotation begins.

Deliverable: Versioned schema + calibration report

02

Annotation guideline

Class definitions with ODD scope. Occlusion handling, partial-visibility policy, distance-bucket thresholds for ground truth.

Deliverable: Annotation guideline document

03

Calibration round

Pilot batch on shared subset. 3D IoU between annotators computed. Camera-LiDAR projection check. Disagreement patterns drive refinement.

Deliverable: Calibration 3D-IoU report

04

IAA gate

Production starts only when calibration clears 3D IoU 0.6+ at near range, center-distance under 0.3m at under 30m.

Deliverable: Gate-pass attestation

05

Production with tracker-assist

Multi-frame interpolation between keyframes. Tracker continuity check. Single-tenant CVAT 3D + proprietary fusion viewer.

Deliverable: Annotated scans + tracking

06

QA + adjudication + delivery

Per-class 3D IoU. BEV mAP. Tracking continuity. Camera-LiDAR projection consistency. Article 30 records, signed DPA, sub-processor list.

Deliverable: Final delivery + metrics report

Six gates. One trail of evidence. Calibration verified before any annotation.

Start the scoping process →
SCENE COVERAGE

Scene types our 3D pipeline ingests.

Six representative driving scenes, each with the annotation class taxonomy we lock with your team before production. Class counts shift per scene type. Production work runs against your raw scans, multi-frame, calibration-verified.

  1. Urban driving

    4 classes
    • Vehicle
    • pedestrian
    • cyclist
    • lane

  2. Highway

    4 classes
    • Vehicle
    • truck
    • lane
    • sign

  3. Parking

    3 classes
    • Vehicle
    • pedestrian
    • curb

  4. Dense urban

    5 classes
    • Vehicle
    • pedestrian
    • cyclist
    • building
    • sign

  5. Roundabout

    3 classes
    • Vehicle
    • lane
    • curb

  6. Construction zone

    4 classes
    • Vehicle
    • barrier
    • cone
    • sign

Scene types and class taxonomies follow automotive ODD conventions used across public driving datasets (A2D2, nuScenes, KITTI). Production annotation runs on your raw scans, multi-frame, calibration-verified, with Article 30 records on delivery.

PUBLIC BENCHMARK COVERAGE

Procurement-grade benchmark coverage.

3D IoU at 0.7 is the procurement-grade threshold for autonomous-driving perception. We hold this on the public splits we have published; customer-deployment numbers are reported under NDA.

Most LiDAR public datasets are research-licensed; A2D2 is the notable commercial-clean exception. Production work runs against your own data under your engagement DPA.

Benchmark Split 3D IoU @ 0.7 Distance buckets Tracking continuity License
KITTI / KITTI-360 KITTI format Test in-house: 94% 0-30, 30-60, 60m+ < 1.6% switch / km Research only CC BY-NC-SA
nuScenes nuScenes JSON Val in-house: 92% 0-30, 30-60, 60m+ < 2.1% switch / km Research only Non-commercial
Waymo Open TFRecord, proto Val supported 0-30, 30-50, 50m+ supported Research only Waymo dataset
A2D2 (Audi) PNG + JSON Full in-house: 89% 0-30, 30-60, 60m+ < 2.4% switch / km Commercial OK CC BY-ND 4.0
SemanticKITTI 25 classes/point Val mIoU 0.71 per-point, no buckets n/a (segmentation) Research only CC BY-NC-SA 4.0
Argoverse 2 AV2 SDK Val supported 0-30, 30-100m supported Mixed terms CC BY-NC-SA 4.0

License status reflects publicly stated terms. Verify per engagement before commercial training use. 'in-house' figures are anonymized customer-deployment results under NDA.

WHAT YOU RECEIVE

Every delivery ships with the artefact pack your Article 10 file needs.

The records a regulated AD or robotics buyer expects with every 3D engagement. No upgrade tier, no separate request.

Annotation guideline + ODD scope.

Versioned guideline with class definitions, occlusion handling, partial-visibility policy, distance-bucket thresholds. ODD scope documented. Every version preserved for audit.

Calibration verification log.

Intrinsic, extrinsic, and PTP-sync timestamp verification before any annotation begins. Calibration drift checks on long sequences. Camera-LiDAR projection consistency report.

Per-class 3D IoU + BEV mAP.

Per-class 3D IoU at multiple thresholds. BEV mAP. Per-distance-bucket accuracy (near, mid, far). Orientation error (yaw, pitch, roll). Center-distance error.

Tracking continuity report.

Multi-frame tracking ID continuity. Track switch rate. Per-track length distribution. Identity switches per kilometre or per frame.

Records of processing, DPA, and sub-processor list.

Article 30 records of processing, signed Article 28 DPA, lawful-basis documentation, 30-day erasure SLA, and full sub-processor list with Article 28(2) change notifications.

START A PROJECT

Brief us. We reply within one business day.

Short brief now, deeper scoping in the reply.

Capability lanes (NER, RLHF, etc.), languages, volume, regulatory context.

QUESTIONS BUYERS ACTUALLY ASK

Frequently asked questions

Camera intrinsic + extrinsic parameters, LiDAR-camera extrinsics, PTP-synchronized timestamps. Calibration verified before annotation begins; drift checks on long sequences. Calibration verification log shipped with every delivery.

Yes. LiDAR + camera (most common), LiDAR + camera + radar, LiDAR + IMU + GPS, LiDAR + thermal. Single 3D box drawn in LiDAR and projected to all cameras. Time-synced multi-frame annotation.

EEA-resident. Norwegian company, EEA contributor network, EEA infrastructure. 30-day GDPR Article 17 erasure SLA. Outside US CLOUD Act reach.

Article 28 processor obligations, EU AI Act Article 10 data governance, ISO 21448 SOTIF data-governance alignment for automotive engagements, EEA-residency, DPA-by-default, single-tenant isolation. Records of processing under GDPR Article 30 ship with every delivery.

KITTI format, nuScenes JSON, custom JSON or Protocol Buffers, vendor formats (Ouster, Velodyne, Hesai), PCD, LAS/LAZ for surveying. Customer-owned work product, no reuse rights retained.

GDPR-Native EU AI Act Article 10 EEA Operations Consent Evidence

Brief us on your 3D perception project.

One business day reply. NDA on request. DPA included.

Fusion-ready 3D Calibration-verified delivery
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