3D Cuboid Annotation Services | LiDAR Labeling

About Us

At a Glance

Built for 3D data teams.

QA tiers on every batch

▲ Annotator, automated and expert review

Delivery regions

▲ US, UK, Europe, APAC and more

15+

Annotation types in one company

▲ Cuboids, polygons, segmentation and more

Our Team

3D Cuboid & LiDAR Annotation from a Pune-Based Team

Since 2017, we have labeled 810M+ images and delivered 15M+ 3D annotations. We produce ground-truth data for autonomous vehicles, robotics, smart cities, AR/VR and AI training pipelines, with every batch passing three QA tiers.

We are a data labeling company based in Pune, with a dedicated LiDAR and 3D team. We label multi-sensor data for vehicle and pedestrian detection, cyclist tracking and environment mapping, and every engagement starts under NDA with no lock-in. 3D cuboid annotation is one of 15+ types in our AI data labeling services, from semantic segmentation to text annotation, under one SLA.

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LiDAR & Point Cloud Labeling

Our annotators label LiDAR data frame by frame and across full sequences.

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Target of Up to 99.8% Accuracy

Peer review, senior checks and rule-based validation on every batch, across all object classes.

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Security & Compliance

Role-based access, NDA-bound workflows and audit trails, with ISO 27001-aligned controls and HIPAA- and GDPR-aligned handling where the data requires it.

Discuss Your 3D Project →

Industries We Serve

Industries Using 3D Cuboid Annotation

We label 3D data for autonomous vehicles, robotics, AR/VR, smart cities and industrial AI.

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Automotive & Autonomous Vehicles

3D cuboid datasets for the perception, vehicle-tracking and collision-avoidance models behind ADAS and self-driving systems. See our dedicated autonomous vehicle annotation service.

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Robotics & Industrial Automation

Point cloud labels for object recognition, navigation and warehouse automation, for robotic arms and mobile robots. Shelf and product labeling in retail settings is covered by our retail annotation service.

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Smart Cities & Traffic Management

We label vehicles, pedestrians and road infrastructure in traffic scenes for traffic analysis, signal optimization and city planning models.

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AR/VR & Simulation Platforms

Object position, orientation and scene depth labeled for AR/VR and virtual training platforms. Pose and keypoint work is available through our landmark & keypoint annotation.

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Defense & Security Applications

We label objects in cluttered outdoor scenes so perimeter-monitoring and threat-detection models can classify them, all under NDA.

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Industrial AI & Safety Monitoring

We label factory floors, robot navigation zones and safety-critical areas in LiDAR data for worker-safety and collision-avoidance models.

Core Technology

What Is 3D Cuboid Annotation
and Why It Matters

3D cuboid annotation places three-dimensional boxes around objects so AI models can judge their size, position and orientation.

3D cuboids placed around objects in a LiDAR point cloud

Labeling objects in 3D space gives models the depth and geometry that flat images can't. That spatial context is what scene understanding in autonomous vehicles, robotics, smart cities and AR/VR depends on.

Well-labeled LiDAR data helps models detect vehicles, pedestrians, cyclists and road obstacles reliably. On AV projects, 3D boxes are often paired with semantic segmentation and lane polyline annotation to build full scene-level training sets for autonomous vehicle perception. Cuboids are often labeled on fused LiDAR, radar and camera data aligned into one spatial map.

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Sequence Labeling & Object Tracking Frame-by-frame labeling with consistent IDs and motion paths across frames

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Multi-Sensor Data Fusion Aligning LiDAR, radar, camera and thermal data into one training set

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Simulation & Model Testing Labeled 3D scenes used to test perception models before deployment

Explore All AI Data Labeling Services → See Our Autonomous Vehicle Annotation Service →

Service Capabilities

3D Cuboid Annotation Capabilities

Here is what our team handles on 3D projects, from cuboids on road objects to custom taxonomies and sensor alignment.

LiDAR point cloud with vehicles and pedestrians labeled by 3D cuboids

3D Object Detection & Tracking

Cuboids placed so models can detect, localize and track objects across 3D frames.

Vehicle, Pedestrian & Cyclist Labeling

Class-specific rules for road users: box size, heading and class for cars, trucks, pedestrians and cyclists, for ADAS and autonomous driving models.

Point Cloud & LiDAR Annotation

Labeling of raw LiDAR scans, including sparse and partly hidden objects, with occlusion flags and track IDs carried across sequences.

Scene Segmentation

Semantic and instance segmentation for roads, buildings, vegetation, signage and other moving or static objects.

Drivable Path & Lane Labeling

Drivable areas, lane edges, curbs and obstacles labeled for autonomous driving models.

AR/VR Object Positioning & Orientation

3D cuboids with rotation labels (yaw or quaternion) for AR/VR scenes, spatial awareness models and virtual training platforms.

Custom Taxonomy & Ontology Setup

We design the taxonomy, class hierarchy and annotation rules around your domain and model.

Sensor Alignment & Synchronization

Synchronizing LiDAR, RGB, radar or thermal inputs so labels line up across sensors.

Choosing the Right Annotation Type

3D Cuboid vs 2D Bounding Box vs Polygon — When to Use Which

Annotation type affects model performance, dataset cost and pipeline complexity. Use this comparison to decide between 3D cuboids, 2D boxes and polygons for your data.

Criteria	3D Cuboid Annotation	2D Bounding Box	Polygon Annotation
Spatial Dimensions	Full 3D: position (X, Y, Z), dimensions and orientation	2D only — no depth or orientation	2D contour — no depth data
Primary Data Type	LiDAR point clouds, RGB-D, multi-sensor fusion	Camera images & video frames	Camera images, satellite, drone
Spatial Precision	Highest — captures true 3D geometry	Object-level only (includes background)	High — follows object contour closely
Annotation Complexity	High — 3D placement + rotation angles	Lowest — fastest to annotate	Moderate — multi-point polygon
Best For	Autonomous driving, robotics, smart cities, ADAS, warehousing	Object detection from camera, retail AI, CCTV	Irregular shapes — equipment, aerial views
Object Tracking	Full temporal 3D tracking across frames	2D frame tracking — no depth continuity	Possible but very high effort
Output	x,y,z coords, w,h,l dimensions, rotation quaternion, class, track ID	x,y,w,h coordinates + class label	Polygon vertex coords + class
Precise BPO Service	This page — 3D Cuboid	Bounding Box Annotation →	Polygon Annotation →

Not sure whether your project needs 3D cuboids or 2D annotation? Send us a sample and we'll recommend an approach based on your sensor data, model and use case. If 2D boxes are enough, our bounding box annotation service covers that, and our guide to common bounding box mistakes is worth a read before you start.

Tool & Platform Compatibility

Tools, Formats & Secure Transfer

We work in your existing toolchain or recommend one that fits the project. We handle data from common LiDAR sensors, including Velodyne, and deliver in KITTI, nuScenes or Waymo Open Dataset formats. No lock-in, no re-tooling.

🖥️ 3D Annotation Platforms

CVAT (3D Point Cloud Mode) Scale AI (LiDAR) Labelbox (3D Sensor Fusion) SuperAnnotate 3D Segments.ai (LiDAR) CloudCompare Custom / In-house Tools

📁 3D Output Formats

PCD (Point Cloud Data) JSON (cuboid coords + quaternions) CSV (structured object metadata) XML KITTI format nuScenes JSON schema Waymo Open Dataset format Custom schema on request

🤖 Stacks Our Datasets Feed Into

PyTorch / PyTorch3D OpenPCDet (3D detection) PointPillars / PointNet++ CARLA Simulation Platform ROS / ROS 2 (robotics)

🔒 Secure Transfer

Encrypted SFTP AWS S3 (private bucket) Google Cloud Storage Azure Blob Storage Secure client portals

Our Process

Our 3D Cuboid Annotation Workflow

Six steps, from agreeing guidelines to final handover.

Requirements Analysis

We agree your objectives, object classes, sensor types and AI goals, then write clear annotation guidelines and measurable success criteria.

Class taxonomy Sensor types AI goal definition SLA setup

Data Preparation

We organize, clean and normalize LiDAR, point cloud and multi-sensor data so labeling starts from consistent inputs.

LiDAR normalization Format checks Batch preprocessing

Annotation & Labeling

Trained annotators place 3D cuboids with position, orientation and object tracks, keeping boxes and IDs consistent across frames.

3D cuboid placement Temporal tracking Guideline-based labeling Occlusion handling

Three-Tier QA

Peer review, senior checks and rule-based validation hold every batch to your guidelines and our target of up to 99.8% accuracy.

IoU validation Automated QC Expert review Sampling audits

Sample Review & Feedback

We share samples, take your engineering feedback and refine the labeling rules as your model requirements change.

Feedback integration Guideline updates Re-annotation cycles Sample reviews

Final Delivery & Scaling

Datasets delivered in JSON, CSV, XML, PCD, KITTI, nuScenes or custom formats, with batch expansion and ongoing delivery as volumes grow.

JSON / PCD / XML CSV / custom Full audit logs

Typical turnaround: 24–48 hours per batch (batch size agreed at scoping)

0–1 hr

Secure Intake

1–8 hrs

Dataset Preprocessing

8–36 hrs

3D Cuboid Annotation

36–44 hrs

Three-Tier QA Review

44–48 hrs

Delivery & Sign-Off

Project Examples

3D Cuboid Annotation Use Cases

Examples from recent 3D labeling projects across autonomous vehicles, warehouse robotics, smart cities, AR/VR, industrial safety and defense. Outcomes listed are as reported by the clients.

🚗 Autonomous Vehicles · US

AV Perception & ADAS Training

Client Need: Label 2M+ LiDAR frames for vehicle, pedestrian and cyclist detection across complex urban and highway scenes for ADAS training pipelines.

Solution: 3D cuboid labeling with three-tier QA, frame-level tracking and sensor fusion across LiDAR and camera inputs.

Higher object detection accuracy on the client's test set

🏭 Warehouse Robotics · EU

Warehouse Navigation & Path Planning

Client Need: Annotate 1.5M+ point cloud frames for obstacle avoidance and robotic navigation in large distribution centers.

Solution: 3D cuboids for static and moving objects, with orientation labels and a class hierarchy built around the warehouse layout.

More efficient path planning in the client's trials
24-hour turnaround on priority batches

🏙️ Smart City Traffic · APAC

Urban Traffic Flow Analysis

Client Need: Annotate 1M+ frames to support traffic-flow analysis, pedestrian tracking and infrastructure classification across several city deployments.

Solution: Multi-sensor 3D labeling with scene segmentation, object tracking and signal-zone classification across frame sequences.

Better congestion prediction accuracy

🥽 AR/VR Simulation · Middle East

Immersive 3D Object Mapping

Client Need: Label 500K+ objects in 3D for immersive VR training simulations with full pose orientation and spatial awareness data.

Solution: 3D cuboids with rotation vectors, pose labels and scene-depth calibration.

Pose and depth labels for 500K+ objects, delivered on schedule

⚙️ Industrial AI · LATAM

Factory Safety & Robotic Arm Guidance

Client Need: Annotate 1M+ LiDAR frames for factory floor safety monitoring, worker proximity detection, and robotic arm navigation precision across several plants.

Solution: 3D cuboid labeling with safety-zone marking and a multi-class taxonomy that handles occlusion in live plant environments.

Safety-zone and proximity labels across all plants

🛡️ Defense & Security · UK

3D Labeling for Perimeter Security

Client Need: Classify objects in cluttered outdoor scenes for a perimeter-security system.

Solution: 3D object labeling with a multi-class taxonomy, run under NDA with access-controlled delivery.

Delivered a labeled object dataset for the client's perimeter-security system

Our Advantage

Why Teams Work With Precise BPO
on 3D Data

What changes when a dedicated 3D team handles your LiDAR labeling.

Precise BPO 3D cuboid annotation and LiDAR labeling team

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Works in Your Tools We work in CVAT, Labelbox, Scale AI or your own platform, with no lock-in.

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Delivering Data Services Since 2008 We have worked across automotive, robotics, AR/VR, smart city, defense and spatial AI projects.

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540+ In-House Team 540+ in-house team members company-wide, with a dedicated 3D team working to your guidelines with peer and senior review.

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15M+ 3D Annotations Delivered We have handled high-volume, multi-frame LiDAR labeling programs, from pilot batches to multi-million-frame projects.

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Security Under NDA Role-based access and audit trails, with an NDA signed before any data is shared.

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Shifts Across Time Zones Our Pune team covers US, UK, European and APAC working hours.

Start a 3D Cuboid Project →

Client Feedback

What Our Clients Say

Feedback from teams in autonomous vehicles, robotics and smart infrastructure.

★★★★★

We gave them a LiDAR backlog our own team had no time to touch. The first sample needed a couple of guideline tweaks, and after that every delivery arrived on the agreed date and needed very little rework. Our ADAS engineers now spend their time on the model instead of fixing labels.

James M.

VP Engineering · Autonomous Vehicles · United States

★★★★★

Three years in, they are still our first call for point cloud labeling. The class rules for our warehouse layouts were set up once and have held ever since. When peak season brings extra volume, they add annotators and we don't have to re-brief anyone.

Sophie K.

AI Project Lead · Industrial Robotics · Germany

★★★★★

Before we shared any data, our procurement team sent over a long list of security questions, and each one got a clear written answer. The labeling then ran to the timeline we had agreed, which kept our smart city rollout on its original dates.

Ahmed T.

Director of AI · Smart Infrastructure · UAE

Quality Assurance

Three-Tier QA: How We Target Up to 99.8% on 3D Data

Every dataset passes three quality gates before it reaches you. Each tier checks something different: human review, automated geometry checks, then an expert audit. That way mistakes are caught before they reach your perception pipeline.

Tier 1 Annotator + Peer Review

→

Tier 2 Spatial Validation

→

Tier 3 Expert Audit + Delivery

Annotator Self-Check & Peer Review

The annotator does the first pass and a senior peer cross-checks it. This catches 3D placement errors, class mismatches, wrong orientation angles and guideline deviations before any automated scoring.

Annotator verifies cuboid tightness, object class, 3D orientation and edge alignment against project-specific labeling guidelines before submitting

Senior annotator cross-checks: position consistency across frames, orientation logic for moving objects and multi-class label correctness in dense scenes

Batches that fail the T1 threshold are returned for correction before advancing to automated spatial validation

T1 Exit Accuracy Target95%+

Orientation Rule Compliance97%+

Automated Spatial Validation & Consistency Checks

An automated layer scores every cuboid against dimensional benchmarks, checks for duplicates, validates orientation across frames and flags statistical outliers in the batch.

3D IoU scoring against reference annotations and a project-specific threshold (typically ≥0.85 for standard, ≥0.90 for ADAS and safety-critical projects)

Duplicate detection: overlapping cuboids for the same object class in the same frame are flagged and checked

Orientation outlier scan: cuboids with anomalous rotation angles, dimension ratios or positional drift flagged for human review

T2 Exit Accuracy Target98%+

IoU Threshold, ADAS & Safety-Critical≥0.90

Expert Audit & Final Delivery

The QA Lead audits a random sample of every batch, and reviews the full batch on safety-critical AV and robotics projects. Client feedback is built in: corrections are applied and re-verified before final sign-off. For how we run QA day to day, see our annotation governance guide.

Random sampling audit: the QA Lead reviews 10–20% of scans per batch (100% on ADAS and other safety-critical projects)

Client sample review: 50–100 annotated frames delivered for acceptance before the full batch proceeds, which avoids rework downstream

Iterative feedback: corrections applied, re-scored through T2 spatial validation, and re-delivered with a full audit trail

Final Delivery Accuracy Target (Up To)99.8%

How We Compare

In-House vs. Typical Outsourcing vs. Precise BPO

Criteria	In-House Team	Typical Outsourcing	Precise BPO
3D / LiDAR Annotation Expertise	❌ Requires specialist hires, which are hard to find	⚠ Limited; most lack 3D annotation depth	✔ Dedicated 3D team covering LiDAR, cuboid and point cloud
Accuracy on 3D Spatial Data	Varies (inconsistent QC on 3D geometry)	Varies (no specialist 3D QA pipeline)	✔ Target of up to 99.8% accuracy, checked per batch with three-tier 3D validation
Getting Started	Slowest: hire 3D specialists, build tooling and pipelines	Faster than hiring, slower than a pilot	✔ Pilot batch first, production after your sign-off
Scalability for LiDAR Surge Volumes	❌ Fixed headcount, no elastic capacity	⚠ Limited, delays common on 3D volume spikes	✔ Large annotator pool, with capacity added as LiDAR volume grows
Cost	Baseline (specialist salaries + tooling)	Moderate savings	✔ 40–60% Below US/UK Rates
Data Security	❌ Varies; often informal for sensor data	⚠ Varies by vendor	✔ ISO 27001-aligned controls, NDA on every project
Multi-Sensor Fusion Support	⚠ Requires separate tooling investment	⚠ Varies — most handle LiDAR only	✔ LiDAR + camera RGB-D + multi-sensor fusion
Platform Agnostic	⚠ Limited to in-house tools	⚠ Often platform-locked	✔ CVAT, Scale AI LiDAR, Labelbox, custom tools
Free Pilot / Trial	❌ Not applicable	❌ Rarely offered	✔ Free pilot batch, no commitment

Pricing

3D Cuboid Annotation Pricing & Engagement Models

No platform fees, no lock-in. Pick the model that fits your volume, timeline and budget; every project starts with a free pilot batch. Our data labeling pricing guide covers typical costs across annotation types.

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Per Frame / Scan

Best for: Defined LiDAR scan batches

Pay per labeled LiDAR frame or point cloud scan. Suits fixed-size datasets, one-off projects and AV teams building a first 3D dataset at a predictable per-frame cost.

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Per Object

Best for: Dense object scenes

Priced per labeled 3D object. Suits dense scenes such as warehouses, intersections and urban driving data, where the number of objects is the natural unit of work.

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Per Hour

Best for: Complex multi-sensor data

Hourly model for high-complexity 3D annotation, multi-sensor fusion alignment, edge-case occlusion scenes or long-tail object classes where per-frame pricing doesn't reflect actual annotator effort.

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Monthly Retainer

Best for: Continuous AV pipelines

A dedicated 3D annotation team at fixed monthly capacity. Suited to AV companies and robotics labs with active sensor data streams and continuous labeling needs.

Volume discounts from 100K+ frames/month.

All models include an NDA, a target of up to 99.8% accuracy and a free pilot batch before you commit.

Get a 3D Cuboid Annotation Quote →

Global Delivery

Delivery Across 8 Regions

Our team in India runs shifts that overlap with working hours in each of these regions, with regional data-handling rules where they apply.

NDA On Every Project

27+ Countries Served

8 Global Regions

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United States

Shifts overlap with US business hours

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United Kingdom

Same-day handoffs in UK hours

GDPR-aligned delivery

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Europe

Germany · France · Netherlands · Sweden · Denmark · Switzerland · Spain

GDPR-aligned delivery

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Australia & New Zealand

Overlap with Australian and NZ working hours

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Canada

Daily overlap with Canadian business hours

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Asia-Pacific

Singapore · Japan · South Korea · Hong Kong · Taiwan · India

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Middle East & Africa

UAE · Saudi Arabia · Israel · South Africa · Kenya

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Latin America

Brazil · Mexico · Argentina · Colombia · Chile

Common Questions

3D Cuboid Annotation — FAQs

3D cuboid annotation marks objects in 3D space with bounding boxes that capture position, size, orientation and depth. It gives perception models real-world geometry, which is why it is used in autonomous driving, robotics, smart cities, AR/VR and warehouse automation, where 2D boxes cannot capture spatial relationships.

We annotate LiDAR point clouds, RGB-D and other depth data, and multi-sensor fusion inputs. All of these capture 3D structure, so models can learn distance, orientation and motion.

Yes. We label fused LiDAR and camera data, aligning the point cloud with the RGB frames so each 3D cuboid is consistent across sensors. AV, ADAS and robotics teams use this to train models that detect and track objects from combined sensor streams.

We maintain accuracy through three QA tiers: annotator and peer review, automated spatial validation, and an expert audit with batch sampling. Cuboid dimensions, orientation and object class are checked against your guidelines, so errors are caught before they reach training.

We deliver 3D cuboid annotations in JSON, CSV, XML, PCD, KITTI, nuScenes or a custom schema that matches your pipeline. Each file includes 3D box coordinates, orientation, class labels and tracking IDs, ready to load for training.

Yes. Our workflows support projects from pilot batches to enterprise scale, including ongoing sensor data streams from autonomous vehicle fleets, robotics deployments, and simulation pipelines. Standard guidelines, dedicated annotator teams and batch-based delivery keep quality steady as volume grows.

Our workflows follow ISO 27001-aligned controls, with HIPAA- and GDPR-aligned handling where the data requires it. All annotators sign NDAs before getting project access, environments are permission-scoped, and security audits run regularly, so your proprietary LiDAR and sensor data stays protected.

Pricing depends on frame or scan volume, object density per scene, annotation complexity and the QA depth required. You can choose per-frame, per-object, hourly or monthly retainer pricing. India-based delivery can cost 40–60% below US/UK rates. Send us your dataset details and we'll quote based on your scope.

Get In Touch

Tell Us About Your
3D Annotation Project

Share your data, timeline and scope, and we'll reply with a project plan and quote. The free pilot starts within 48 hours after the NDA is signed, and pilot delivery takes 24–48 hours, where applicable.

Contact Information

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Phone & WhatsApp +91 79726 20994

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Email info@precisebposolution.com

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Website www.precisebposolution.com

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Office B3, Sant Namdev Marg, Varundavan Colony, Walhekarwadi, Gurudwara Colony, Nigdi, Pimpri-Chinchwad, Pune, Maharashtra 411033, India

NDA on every project, with ISO 27001-aligned security controls.

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✅

Thank You — Message Sent!

Our 3D annotation experts will review your project requirements and get back to you within one business day.

Browse our full range of annotation services →

3D Cuboid Annotation Services for AI & Computer Vision

3D Cuboid & LiDAR Annotation from a Pune-Based Team

Industries Using 3D Cuboid Annotation

Automotive & Autonomous Vehicles

Robotics & Industrial Automation

Smart Cities & Traffic Management

AR/VR & Simulation Platforms

Defense & Security Applications

Industrial AI & Safety Monitoring

What Is 3D Cuboid Annotationand Why It Matters

3D Cuboid Annotation Capabilities

3D Object Detection & Tracking

Vehicle, Pedestrian & Cyclist Labeling

Point Cloud & LiDAR Annotation

Scene Segmentation

Drivable Path & Lane Labeling

AR/VR Object Positioning & Orientation

Custom Taxonomy & Ontology Setup

Sensor Alignment & Synchronization

3D Cuboid vs 2D Bounding Box vs Polygon — When to Use Which

Tools, Formats & Secure Transfer

Our 3D Cuboid Annotation Workflow

Requirements Analysis

Data Preparation

Annotation & Labeling

Three-Tier QA

Sample Review & Feedback

Final Delivery & Scaling

3D Cuboid Annotation Use Cases

AV Perception & ADAS Training

Warehouse Navigation & Path Planning

Urban Traffic Flow Analysis

Immersive 3D Object Mapping

Factory Safety & Robotic Arm Guidance

3D Labeling for Perimeter Security

Why Teams Work With Precise BPOon 3D Data

What Our Clients Say

Three-Tier QA: How We Target Up to 99.8% on 3D Data

Annotator Self-Check & Peer Review

Automated Spatial Validation & Consistency Checks

Expert Audit & Final Delivery

In-House vs. Typical Outsourcing vs. Precise BPO

3D Cuboid Annotation Pricing & Engagement Models

Delivery Across 8 Regions

3D Cuboid Annotation — FAQs

Tell Us About Your3D Annotation Project

Contact Information

Thank You — Message Sent!

Related Annotation Services

Guides on Data Labeling & Annotation

3D Cuboid Annotation
Services for AI & Computer Vision

What Is 3D Cuboid Annotation
and Why It Matters

Why Teams Work With Precise BPO
on 3D Data

Tell Us About Your
3D Annotation Project