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We present a Python-based renderer built on NVIDIA's OptiX ray tracing engineand the OptiX AI denoiser, designed to generate high-quality synthetic imagesfor research in computer vision and deep learning. Our tool enables thedescription and manipulation of complex dynamic 3D scenes containing objectmeshes, materials, textures, lighting, volumetric data (e.g., smoke), andbackgrounds. Metadata, such as 2D/3D bounding boxes, segmentation masks, depthmaps, normal maps, material properties, and optical flow vectors, can also begenerated. In this work, we discuss design goals, architecture, andperformance. We demonstrate the use of data generated by path tracing fortraining an object detector and pose estimator, showing improved performance insim-to-real transfer in situations that are difficult for traditionalraster-based renderers. We offer this tool as an easy-to-use, performant,high-quality renderer for advancing research in synthetic data generation anddeep learning.

NViSII: A Scriptable Tool for Photorealistic Image Generation