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