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A Review of Techniques for 3D Reconstruction of Indoor Environments

by Zhizhong Kang 1,2,*, Juntao Yang 1,2, Zhou Yang 1,2 and Sai Cheng 1,2
School of Land Science and Technology, China University of Geosciences, No. 29 Xueyuan Road, Haidian District, Beijing 100083, China
Shanxi Key Laboratory of Resources, Environment and Disaster Monitoring, No. 380 Yingbin West Street, Yuci District, Jinzhong 030600, China
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(5), 330;
Received: 26 January 2020 / Revised: 7 May 2020 / Accepted: 15 May 2020 / Published: 19 May 2020
(This article belongs to the Special Issue State-of-the-Art in Spatial Information Science)
Indoor environment model reconstruction has emerged as a significant and challenging task in terms of the provision of a semantically rich and geometrically accurate indoor model. Recently, there has been an increasing amount of research related to indoor environment reconstruction. Therefore, this paper reviews the state-of-the-art techniques for the three-dimensional (3D) reconstruction of indoor environments. First, some of the available benchmark datasets for 3D reconstruction of indoor environments are described and discussed. Then, data collection of 3D indoor spaces is briefly summarized. Furthermore, an overview of the geometric, semantic, and topological reconstruction of the indoor environment is presented, where the existing methodologies, advantages, and disadvantages of these three reconstruction types are analyzed and summarized. Finally, future research directions, including technique challenges and trends, are discussed for the purpose of promoting future research interest. It can be concluded that most of the existing indoor environment reconstruction methods are based on the strong Manhattan assumption, which may not be true in a real indoor environment, hence limiting the effectiveness and robustness of existing indoor environment reconstruction methods. Moreover, based on the hierarchical pyramid structures and the learnable parameters of deep-learning architectures, multi-task collaborative schemes to share parameters and to jointly optimize each other using redundant and complementary information from different perspectives show their potential for the 3D reconstruction of indoor environments. Furthermore, indoor–outdoor space seamless integration to achieve a full representation of both interior and exterior buildings is also heavily in demand. View Full-Text
Keywords: indoor environment; geometric modeling; semantic modeling; topological modeling; scene reconstruction indoor environment; geometric modeling; semantic modeling; topological modeling; scene reconstruction
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Kang, Z.; Yang, J.; Yang, Z.; Cheng, S. A Review of Techniques for 3D Reconstruction of Indoor Environments. ISPRS Int. J. Geo-Inf. 2020, 9, 330.

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