Structured3D: A Large Photo-realistic Dataset for Structured 3D Modeling
2020-08
会议录名称EUROPEAN CONFERENCE ON COMPUTER VISION
发表状态已发表
DOI10.1007/978-3-030-58545-7_30
摘要

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 largescale 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 industryleading 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.

关键词Dataset 3D structure Photo-realistic rendering
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收录类别EI ; CPCI
语种英语
文献类型会议论文
条目标识符https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/124048
专题信息科学与技术学院_博士生
信息科学与技术学院_PI研究组_高盛华组
信息科学与技术学院_硕士生
作者单位
1.KooLab, Kujiale.com
2.ShanghaiTech University
3.Shanghai Engineering Research Center of Intelligent Vision and Imaging
4.The Pennsylvania State University
第一作者单位上海科技大学
推荐引用方式
GB/T 7714
Jia Zheng,Junfei Zhang,Jing Li,et al. Structured3D: A Large Photo-realistic Dataset for Structured 3D Modeling[C],2020.
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