Autonomous Driving Sensor 2D · 3D Cuboid Data Labeling Case Study

Autonomous Driving · Sensor Data Labeling

Autonomous Driving Sensor 2D · 3D Cuboid Data Labeling Case Study

Industry: Autonomous Driving Mobility (Smart Mobility) · Project: Autonomous Driving Pilot Zone Infrastructure Sensor Data

Project Overview

AI training dataset construction project for LiDAR and Radar-based autonomous driving infrastructure.

Project Title

Autonomous Driving LiDAR · Radar 2D · 3D Cuboid Data Labeling Construction

• Data Type: LiDAR · Radar-based image and point cloud sensor data / JSON delivery
• Labeling Scope: 2D bounding boxes, 3D cuboids, 2D–3D object ID matching, road object category classification
• Application Area: Advanced AI training dataset for autonomous driving pilot zone infrastructure
• Key Challenges: High-complexity 3D labeling, maintaining object ID consistency, standardizing labeling quality across multiple annotators
• Scale: Real-road driving sensor data, thousands to tens of thousands of processed data instances

Key Work Scope

A precision data labeling project including high-complexity 3D cuboids and 2D–3D ID matching.

TASK	Description
Requirements Analysis & Guideline Development	Defined road object categories, 2D·3D labeling standards, and ID matching principles based on client requirements. Developed detailed guidelines and conducted internal annotator training.
Road Object Category Classification	Classified vehicles, pedestrians, two-wheelers, traffic signals, and road signs according to the defined schema. Established clear handling standards for ambiguous cases.
2D Bounding Box Labeling	Identified road objects within images and generated precise 2D bounding boxes, considering overlap and occlusion. Reduced annotator variance through repeated sample reviews and feedback.
3D Cuboid Labeling	Generated 3D cuboids reflecting real-world object size and orientation in the point cloud. Managed alignment using minimum point count, height, and width criteria per category.
2D–3D Object ID Matching	Matched IDs between 2D bounding boxes and 3D cuboids for the same object, enabling integrated AI training dataset structures for sensor fusion and tracking model training.
Quality Inspection & JSON Delivery	Conducted both full and sample-based reviews to verify label consistency, missing IDs, and misclassifications. Delivered final outputs in client-specific JSON schema.

Process Flow

1 Requirement Definition · Schema Alignment

2 Guideline Design & Sample Labeling

3 2D Bounding Box Labeling

4 3D Cuboid Labeling

5 2D–3D ID Matching & 1st · 2nd Review

6 Final Dataset Construction & JSON Delivery

Gendive Partner Data Labeling Services

Specialized partner for high-complexity sensor data projects in autonomous driving, mobility, and smart city domains.

What Differentiates Gendive

With proven experience in high-complexity 3D and multi-sensor projects, we reliably handle 3D cuboids and ID matching beyond the capabilities of typical labeling vendors.
Through structured requirement definition, guideline design, and multi-stage quality review, we maintain consistent labeling quality even in large-scale workforce deployments.
We provide flexible customization aligned with client formats and workflows, ensuring seamless integration into existing development and operational environments.

If you are preparing an autonomous driving, mobility, or smart city data labeling project, we recommend reviewing requirements together from the early planning stage. We align scope, budget, and timeline to propose a practical AI training dataset strategy.

For specific consultation or project inquiries, please contact us through the channel below. Our team will review your project requirements and respond accordingly.

Request a Data Labeling Consultation

Request AI Training Dataset Construction

• Contact: Gendive Data Team
• Online Inquiry: Go to Contact Form

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