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Case Study
Adding 3D and Point-Cloud Annotation to a Widely Used CV Tool
The client's tool gained advanced 3D capabilities while keeping the ease of use that made it popular in the first place.
Industry
AI, Data & MLOps
Time Frame
2 Months
Services We offer
Computer Vision
Frontend / 3D
Python
Data Tooling
About the client
A global leader in semiconductor technology.
Problem Statement
An annotation tool that excelled at 2D needed first-class 3D support for autonomous systems, robotics and complex datasets, without disrupting the experience its users already relied on.
The Challenges
3D without disruption
Adding point-cloud annotation had to preserve the 2D experience users already depended on.
Formats that matter
Support for Velodyne and KITTI made the tool useful for real autonomous-systems datasets.
Approach
We extended the tool rather than replacing it:
- 3D rendering and point-cloud annotation
- Seamless switching between 2D and 3D views
- Referential image mapping for context and precision
- Drag-and-drop workflow and support for Velodyne and KITTI formats
The Solution
Users annotate and navigate point clouds, switch between 2D and 3D to visualize complex data, and lean on multiple viewpoints and shortcuts to work faster, built with Three.js on a Django and Postgres backend, containerized for deployment.
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Get Detailed Case Study
Adding 3D and Point-Cloud Annotation to a Widely Used CV Tool
Integrating 3D rendering and point-cloud annotation into an established 2D annotation tool, so users move seamlessly between 2D and 3D for autonomous-systems and robotics datasets.
Capabilities
Product Engineering
AI, Data & Intelligent Systems
UX & Product Design
Experience Engineering
Cybersecurity
Compliance Engineering
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