3D Sensor Fusion

Use Cases

Computer Vision

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    Detection & Tracking

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    Prediction & Planning

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    Lane & Boundary Detection

How it works

Easy to Start, Optimize and Scale

Label all cars, pedestrians, and cyclists in each frame.

2  instruction: 'Label all cars, pedestrians, and cyclists in each frame.',
3  labels: ['car', 'pedestrian', 'cyclist'],
4  meters_per_unit: 2.3,
5  max_distance_meters: 30
6}, (err, task) => {
7    // do something with task
Run Extraction
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    ML-Powered Data Labeling

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    Automated Quality Pipeline

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    Sensor Agnostic

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    Comprehensive Label Support

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    Infinitely Long Tasks (Beta)

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    Attributes Support

Quality Assurance

Best-In-Class Quality

Super Human Quality

3D Sensor Fusion tasks submitted to the platform are first pre-labeled by our proprietary ML-model, then manually reviewed by highly trained workers depending on the ML model confidence scores. All tasks receive additional layers of both human and ML-driven checks.

The resulting accuracy is consistently higher than what a human or synthetic labeling approach can achieve independently.

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