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The goal of this paper is to take a single 2D image of a scene and recoverthe 3D structure in terms of a small set of factors: a layout representing theenclosing surfaces as well as a set of objects represented in terms of shapeand pose. We propose a convolutional neural network-based approach to predictthis representation and benchmark it on a large dataset of indoor scenes. Ourexperiments evaluate a number of practical design questions, demonstrate thatwe can infer this representation, and quantitatively and qualitativelydemonstrate its merits compared to alternate representations.

Factoring Shape, Pose, and Layout from the 2D Image of a 3D Scene