synbilds

Synbilds are a class of synthetic images produced for research and development in computer vision and robotics. The term combines synthetic with Bild, a nod to images generated under controlled conditions, emphasizing explicit control over content, structure, and appearance. Synbilds are typically used to create richly annotated datasets with precise ground-truth information.

Origin and concept: The concept of Synbilds emerged in the machine-vision community as a way to bridge gaps between real-world data and algorithmic needs. By focusing on semantic structure and controllable variation, Synbilds aim to enable systematic experimentation and reproducibility in vision tasks.

Technology and methods: Synbilds rely on 3D scene modeling, parameterized lighting, materials, and camera configurations. A scene graph or semantic map guides the content, while rendering engines produce photorealistic or stylized images. The process often includes annotations such as object boundaries, depth, and pose. Domain randomization and weather or time-of-day variations are commonly applied to increase diversity and improve generalization to real-world data. Some approaches integrate physics-based rendering to simulate motion, occlusion, and realistic material responses.

Applications: They are used to augment training datasets for autonomous driving, robotics, drone vision, and medical imaging simulations, as well as for AR/VR content generation. Synbilds can support privacy by substituting or augmenting real footage with labeled synthetic equivalents and facilitate controlled experimentation.

Advantages and challenges: Advantages include scalability, exact ground truth, and reproducibility. Challenges involve the realism gap between synthetic and real images, potential synthetic biases, and the computational costs associated with high-fidelity rendering.

See also: synthetic data, domain randomization, sim-to-real transfer, computer vision datasets.