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

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12354)

Abstract

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.

Keywords

Dataset 3D structure Photo-realistic rendering 

Notes

Acknowledgements

We would like to thank Kujiale.com for providing the database of house designs and the rendering engine. We especially thank Qing Ye and Qi Wu from Kujiale.com for the help on the data rendering. This work was partially supported by the National Key R&D Program of China (#2018AAA0100704) and the National Science Foundation of China (#61932020). Zihan Zhou was supported by NSF award #1815491.

Supplementary material

504446_1_En_30_MOESM1_ESM.pdf (47.7 mb)
Supplementary material 1 (pdf 48882 KB)

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.KooLab, Kujiale.comHangzhouChina
  2. 2.ShanghaiTech UniversityShanghaiChina
  3. 3.Shanghai Engineering Research Center of Intelligent Vision and ImagingShanghaiChina
  4. 4.The Pennsylvania State UniversityState CollegeUSA

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