Empowering your computer vision team with speedy, accurate data labeling
In the world of delivery robotics, the pathway to optimal autonomous navigation pivots on impeccably labeled data. But this process is labor intensive, with room for human errors and inconsistencies. Multi-sensor labeling will optimize the annotation process and reduce the time spent on making corrections.
Navigate the bustling urban scenes with absolute assurance with consistent tracking IDs for your ML models.
Persistent object ID: Whether it’s objects in a packed city sidewalk or a tranquil park path, maintain consistent object identification through diverse scenes.
Maximized efficiency: Accelerate your deployment by reducing reconciliation durations and boosting model precision.
Occlusion management: Ensure continuous labeling by skillfully managing occlusions and maintaining object identification during interruptions.
Go faster from POC to production to scale by labeling multiple sensor in 1 effort, only requiring minimal corrections
Simplified labeling: Project from 3D to 2D data with a click, accessing pre-labeled datasets on all cameras that require minimal fine-tuning.
Flexible exporting: Instance, semantic or panoptic? Export your data in multiple formats depending on your needs.
Improve your delivery robot’s navigation and ensure safe, efficient parcel delivery.
Augmented labeling context: Blend 2D and 3D sensor data, sharpening object differentiation and enhancing labeling precision—key to identifying drivable spaces amidst obstacles.
Smart labeling workflows: Maintain labeling uniformity by enabling a single annotator to label all sensor data in a single interface.
Accurate object classification: Ensure correct object classification for your project with rapid, accurate object identification across all delivery terrains.
Label all your sensor’s data in one interface
Label fast, accurate and efficiently with ML-assisted features build for and by machine learning teams.
