Label quality in synthetic pipelines
Synthetic Data teams often struggle with keeping synthetic labels trustworthy. The gap between a demo and a production system is usually in data coverage,
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Computer Vision
Perception systems, 3D vision, and multimodal understanding.
Synthetic data for vision: when it helps
Synthetic Data teams often struggle with deciding where synthetic data adds value. The gap between a demo and a production system is usually in data
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Tracking across cameras: IDs, re-ID, and drift
Detection and Tracking teams often struggle with keeping track consistency in real deployments. The gap between a demo and a production system is usually
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Object detection metrics you actually need
Detection and Tracking teams often struggle with measuring detectors beyond a single score. The gap between a demo and a production system is usually in
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Edge vision inference: speed vs. accuracy
Computer Vision teams often struggle with balancing latency with quality at the edge. The gap between a demo and a production system is usually in data
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Designing a robust vision classifier
Computer Vision teams often struggle with creating resilient vision models. The gap between a demo and a production system is usually in data coverage,
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3D reconstruction pipelines for products
3D Vision teams often struggle with building reliable 3D capture workflows. The gap between a demo and a production system is usually in data coverage,
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From 2D to 3D: depth estimation basics
3D Vision teams often struggle with translating images into usable depth. The gap between a demo and a production system is usually in data coverage,
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