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Recently, there has been growing interest in developing learning-basedmethods to detect and utilize salient semi-global or global structures, such asjunctions, lines, planes, cuboids, smooth surfaces, and all types ofsymmetries, for 3D scene modeling and understanding. However, the ground truthannotations are often obtained via human labor, which is particularlychallenging and inefficient for such tasks due to the large number of 3Dstructure instances (e.g., line segments) and other factors such as viewpointsand occlusions. In this paper, we present a new synthetic dataset,Structured3D, with the aim of providing large-scale photo-realistic images withrich 3D structure annotations for a wide spectrum of structured 3D modelingtasks. We take advantage of the availability of professional interior designsand automatically extract 3D structures from them. We generate high-qualityimages with an industry-leading rendering engine. We use our synthetic datasetin combination with real images to train deep networks for room layoutestimation and demonstrate improved performance on benchmark datasets.

Structured3D: A Large Photo-realistic Dataset for Structured 3D Modeling