Rendered.ai

Founded 2019

Generate sensor-accurate synthetic training data and annotations for computer vision models

Pricing model

Paid

You can use Rendered.ai to create physically accurate synthetic imagery to train and validate computer vision models. It allows engineering teams to simulate complex sensor data like synthetic aperture radar, infrared, hyperspectral, and X-ray systems. You can configure 3D environments, adjust lighting and weather conditions, and automatically generate annotated datasets with pixel-perfect ground truth labels. The system also supports auto-labeling real-world image datasets, testing models against edge cases, and simulating scenarios where real data collection is dangerous, restricted, or difficult to obtain.

What Rendered.ai does

Configure physics-based lighting and materials in 3D scenesSimulate synthetic aperture radar and hyperspectral sensor outputsGenerate pixel-level segmentation masks and bounding boxes automaticallySet custom camera angles and weather parametersTrain deep learning vision models on synthetic image datasetsExport labeled image batches into machine learning training pipelinesAnnotate unlabelled real-world imagery using synthetic modelsSimulates non-visible sensor types including synthetic aperture radar, thermal, and X-rayAutomatically generates ground-truth labels alongside synthesized imagesAdjusts environmental variables like weather, lighting, and viewing anglesCreates rare edge-case scenarios that are difficult to capture in the real worldIntegrates physics simulators such as DIRSIG and NVIDIA OmniverseProvides automated dataset labeling for real-world computer vision collectionsIncludes tools for training and validating vision models directly on simulated data

Pricing breakdown

Pricing model: Paid

PlanList price

Teams$5,000 / mo

Organizations$15,000 / mo

Frequently asked

DIRSIG, NVIDIA Omniverse, QSIM RT, DIRSIG (Digital Imaging and Remote Sensing), AWS Cloud Infrastructure, Docker Container Support, Python SDK, Jupyter Notebooks, OpenCV

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