We propose the configurable rendering of massive quantities of photorealistic images with ground truth for the purposes of training, benchmarking, and diagnosing computer vision models. In contrast to the conventional (crowd-sourced) manual labeling of ground truth for a relatively modest number of RGB-D images captured by Kinect-like sensors, we devise a non-trivial configurable pipeline of algorithms capable of generating a potentially infinite variety of indoor scenes using a stochastic grammar, specifically, one represented by an attributed spatial And-Or graph. We employ physics-based rendering to synthesize photorealistic RGB images while automatically synthesizing detailed, per-pixel ground truth data, including visible surface depth and normal, object identity and material information, as well as illumination. Our pipeline is configurable inasmuch as it enables the precise customization and control of important attributes of the generated scenes. We demonstrate that our generated scenes achieve a performance similar to the NYU v2 Dataset on pre-trained deep learning models. By modifying pipeline components in a controllable manner, we furthermore provide diagnostics on common scene understanding tasks; eg., depth and surface normal prediction, semantic segmentation, etc.

采用基于学习的方法生成大规模的合成三维场景和任意数量的真实感二维图像，用于训练、基准测试和诊断学习基础的计算机视觉和机器人算法。

使用随机文法进行可配置的 3D 场景合成和 2D 图像渲染，同时提供每个像素的真实值