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

本文介绍了一种新的人工合成数据集 Structured3D，为各种结构化三维建模任务提供大规模照片级真实感图像和丰富的三维结构注释，此外并利用该数据集与真实图像来训练深度神经网络，显著提高了房间布局估计性能。