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.

该论文介绍了一种基于NVIDIA OptiX光线追踪引擎和OptiX AI降噪器的Python渲染器，用于生成高质量的合成图像，为计算机视觉和深度学习的研究提供了帮助。它可以描述和操作包含复杂动态3D场景的对象网格，材料，纹理，光照，体积数据和背景，并生成元数据。该工具可用于生成2D/3D边界框，分割掩码，深度图，法线图，材质属性和光流矢量。该论文还表明，利用路径追踪生成的数据用于训练目标检测器和姿态估计器，可以在某些传统基于光栅的渲染器难以处理的情况下，改善从模拟到真实世界的转换表现。该工具是易于使用，性能高，供推动合成数据生成和深度学习研究的渲染器。

NViSII：用于生成照片级渲染图像的脚本化工具