-
Services
computer vision
self-serve
- Industries
- Pricing
- About
- Careers
-
Resources
- talk to us
3D Vision Software
Polygon
Annotation for
Nodar Depth Models
USA
Overview
Nodar required high-precision polygon annotations to train and validate depth-mapping models for automotive, agriculture, and industrial use cases:
Client
3D computer vision company building depth-mapping software for autonomous driving, industrial use, and agriculture.
Challenges
Needed large-scale polygon labels across sensor-specific scenes; internal team lacked capacity to handle volume and complexity.
Solutions
Labeled up to 2k images per set and ~60k objects using CVAT and client tools, with pilot feedback, layered masks, and full QA.
Results
Helped reduce validation cycles and hit deployment deadlines; workflow scaled easily across multiple parallel model projects.
Client
The US-based computer vision company building 3D depth perception software for autonomous driving, industrial automation, and agriculture.
Challenges
Nodar needed fast, consistent polygon labeling across diverse datasets.
Public datasets didn’t cover sensor and domain-specific edge cases
Internal team lacked resources for high-volume manual annotation
Work had to support several model pipelines in parallel
Solution
Label Your Data handled full-cycle annotation, combining CVAT with Nodar’s internal tooling.
Annotated roads, vehicles, terrain, airport equipment, and people
Created layered polygon masks for depth modeling
Used structured workflows:
Detailed docs pilot task
Live training call
QA feedback loop
Turnaround time: 3-4 weeks per 1-2k-image dataset
Training
A pilot task kicked off each batch. Label Your Data refined
guidelines through client feedback, then scaled to 10-20 annotators with 2-3 QA reviewers.
Edge cases were tracked in shared logs.
The team ran multiple projects in parallel with no rework.
Results
Labeled ~60k polygon masks across datasets
High-quality output helped reduce validation cycles and speed up deployment
Projects stayed on schedule
Scalable team size made it easy to ramp up or down as model demands changed
Request a pilot
Tell us more about your project and data
Thank you for contacting us!
We'll get back to you shortly
I'm impressed with how easy communication is and how simple and effective the process has been. Everything is delivered on time and handled professionally.
Piotr Swierczynski
Vice President of Engineering
Trusted by ML Professionals
Why AI Teams Choose Label Your Data
Data Annotation for Complex Environments
Rely on consistent, high-quality output for complex datasets, detailed taxonomies, and edge cases.
Structured Quality from Pilot to Production
Get quality engineered into every step through onboarding, evolving guidelines, QA, and continuous feedback.
Flexible and Scalable Operations
Adjust team capacity, project size, and delivery model as you scale, with no setup fees or long-term lock-ins.
An Integrated Delivery Partner
Align on goals, workflows, and expectations with a team that integrates into your process from day one.
Projects Led by Annotation Experts
Work with former annotators who understand annotation complexity, quality standards, and high-volume delivery.