Top–Down Bayesian Inference of Indoor Scenes

Abstract

The task of inferring the 3D layout of indoor scenes from images has seen many recent advancements. Understanding the basic 3D geometry of these environments is important for higher level applications, such as object recognition and robot navigation. In this chapter, we present our Bayesian generative model for understanding indoor environments. We model the 3D geometry of a room and the objects within it with non-overlapping 3D boxes, which provide approximations for both the room boundary and objects like tables and beds. We separately model the imaging process (camera parameters), and an image likelihood, thus providing a complete, generative statistical model for image data. A key feature of this work is using prior information and constraints on the 3D geometry of the scene elements, which addresses ambiguities in the imaging process in a top–down fashion. We also describe and relate this work to other state-of-the-art approaches, and discuss techniques that have become standard in this field, such as estimating the camera pose from a triplet of vanishing points.