NViSII: A Scriptable Tool for Photorealistic Image Generation

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 containin...

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Bibliographic Details
Published inarXiv.org
Main Authors Morrical, Nathan, Tremblay, Jonathan, Lin, Yunzhi, Tyree, Stephen, Birchfield, Stan, Pascucci, Valerio, Wald, Ingo
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 28.05.2021
Subjects
Computer vision
Deep learning
Image processing
Image quality
Image segmentation
Material properties
Optical flow (image analysis)
Optical properties
Ray tracing
Renderers
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Summary: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.
ISSN:2331-8422