JCVIsyn30

JCVIsyn30 is a modular software framework and dataset designed to enable controlled studies of synthetic image generation and related computer vision tasks. It provides a standard scene description language, an extensible renderer interface, and an accompanying benchmark suite intended to improve reproducibility in synthetic-data research.

Design and components. The framework includes a core scene graph with 30 canonical templates, a parameterization scheme for camera setup, lighting, textures, and object properties, and a plugin-based rendering back end. The templates span indoor, outdoor, and mixed environments, each offering multiple variation seeds to produce diverse samples. Evaluation metrics cover perceptual similarity, diversity, depth estimation accuracy, and segmentation consistency.

Development and usage. JCVIsyn30 originated from a collaboration of researchers in computer vision and computer graphics to address reproducibility challenges in synthetic-data experiments. It is used to generate datasets, run vision pipelines, and compare method performance under standardized conditions. The typical workflow includes dataset generation, model training or inference, metric computation, and result reporting.

Limitations and reception. Although designed for consistency, critics note that synthetic data may not capture all real-world factors. Users are advised to supplement experiments with real imagery and to document renderer settings, seeds, and evaluation protocols to ensure comparability across studies.

See also: synthetic data, image synthesis benchmark, computer vision benchmarks.