Given the raw data, we recognize depth of objects by annotating 3D cuboid on the objects of interest.
Used to train computer vision models for spatial cognition from 2D images or videos. Relative distance of each mobile object from the ego car and vanishing point can be measured.
Used to build 3D simulated worlds from 2D information captured by cameras.
Our engineers work so hard to fulfill all your custom requirements. Starting from supporting various sensor data formats to building complex annotation tools. We also set up unique data sharing modes so that you can focus on building and optimizing ML models.
With rigour quality checks, free repetition allowance, high-quality ground truth annotation and end-to-end project management we're confident you'll love us.
Definition, Process Excellence and Transparency are the three pillars of our accuracy promise.
Full suite of annotation tools with the help of project consulting, dedicated project management and tech support.
Playment uses machine learning models to perform semi-automatic labeling at a fraction of the cost of manual labeling.
We were very impressed with Playment’s ability to grasp complex requirements and quickly build custom tools to support it. Jitesh(Engagement Manager) was very helpful with sharing his domain expertise to formulate the solution.
Playment's fully managed approach has been critical in factoring the variability and scaling up our annotation requirements. Thanks to their tools and skilled workforce, we are extremely satisfied by the quality and turnaround time they have provided. I would highly recommend Playment.