3D Vision Software
Nodar required high-precision polygon annotations to train and validate depth-mapping models for automotive, agriculture, and industrial use cases:
3D computer vision company building depth-mapping software for autonomous driving, industrial use, and agriculture.
Needed large-scale polygon labels across sensor-specific scenes; internal team lacked capacity to handle volume and complexity.
Labeled up to 2k images per set and ~60k objects using CVAT and client tools, with pilot feedback, layered masks, and full QA.
Helped reduce validation cycles and hit deployment deadlines; workflow scaled easily across multiple parallel model projects.
The US-based computer vision company building 3D depth perception software for autonomous driving, industrial automation, and agriculture.
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
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:
Turnaround time: 3-4 weeks per 1-2k-image dataset
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.
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
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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
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