Supplementary Material for Human-centric Indoor Scene Synthesis Using Stochastic Grammar

Human-centric Indoor Scene Synthesis Using Stochastic Grammar

We present a human-centric method to sample and synthesize 3D room layouts and 2D images thereof, to obtain large-scale 2D/3D image data with the perfect per-pixel ground truth. An attributed spatial And-Or graph (S-AOG) is proposed to represent indoor scenes. The S-AOG is a probabilistic grammar model, in which the terminal nodes are object entities including room, furniture, and supported obj...

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 alg...

Integrating Function, Geometry, Appearance for Scene Parsing

In this paper, we present a Stochastic Scene Grammar (SSG) for parsing 2D indoor images into 3D scene layouts. Our grammar model integrates object functionality, 3D object geometry, and their 2D image appearance in a Function-Geometry-Appearance (FGA) hierarchy. In contrast to the prevailing approach in the literature which recognizes scenes and detects objects through appearance-based classifi...

Facilitating and Exploring Planar Homogeneous Texture for Indoor Scene Understanding (Supplementary Material)

Human Centered Scene Understanding Based on 3D Long-Term Tracking Data

Scene understanding approaches are mainly based on geometric information, not considering the behavior of humans. The proposed approach introduces a novel human-centric scene understanding approach, based on long-term tracking information. Long-term tracking information is filtered, clustered and areas offering meaningful functionalities for humans are modeled using a kernel density estimation....