📈 “Estimates suggest that more than 60 percent of data used for AI applications in 2024 was synthetic, and this figure is expected to grow across industries,” according to MIT News.
This shift is changing how teams build computer vision models. When real-world data is scarce, costly, poorly labeled, or does not exist, quality synthetic data effectively fills the gap—but only if tailored to each use case and generated with physical accuracy by design for any CV sensor modality.
Rendered.ai takes a physics-based approach to synthetic image generation, enabling rapid customization of the backgrounds, objects, material principles, and atmospheric conditions of each model training scenario simulated to match exactly to the CV sensor required. 🚀
🔍 Check out examples of synthetic imagery generated by the Rendered.ai team for the latest emerging use cases—delivered in a matter of days—including:
◾ Fully-labeled, customized synthetic images matched accurately to complex sensor modalities, such as SAR, IR, multispectral, hyperspectral, and X-ray.
◾ Sophisticated domain-specific data generation enhanced with best-in-class simulators integrated into Rendered.ai's Synthetic Data PaaS, such as Rochester Institute of Technology's DIRSIG™, NVIDIA Omniverse, Quadridox, Inc.’s QSim RT, and Rendered.ai's own advanced SAR simulator.
📥 Explore the latest version of Rendered.ai's Synthetic Imagery Lookbook now: https://hubs.li/Q03MpDbZ0
Access the right data to train performant CV models faster, right now. Reach out to our experts to see how quickly you can get training data tailored to your use case: https://hubs.li/Q03MpG0Q0
#syntheticdata #computervision #satelliteimagery #SAR #Xray Massachusetts Institute of Technology