Configurable, Photorealistic Image Rendering and Ground Truth Synthesis by Sampling Stochastic Grammars Representing Indoor Scenes

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 (crowdsourced) 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; e.g., depth and surface normal prediction, semantic segmentation, etc.