Building Floorplan Reconstruction Based on Integer Linear Programming

The reconstruction of the floorplan for a building requires the creation of a two-dimensional floorplan from a 3D model. This task is widely employed in interior design and decoration. In reality, the structures of indoor environments are complex with much clutter and occlusions, making it difficult...

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Vydáno v:	Remote sensing (Basel, Switzerland) Ročník 14; číslo 18; s. 4675
Hlavní autoři:	Wang, Qiting, Zhu, Zunjie, Chen, Ruolin, Xia, Wei, Yan, Chenggang
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Basel MDPI AG 01.09.2022
Témata:	Algorithms Architectural elements Architecture Automation Clutter data collection Datasets Deep learning Finite element method Flooring floorplan reconstruction Floorplans Image reconstruction Indoor environments Integer programming interior design Linear programming Manhattan Methods normalization Optimization planes point cloud Semantics Three dimensional models
ISSN:	2072-4292, 2072-4292
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Abstract	The reconstruction of the floorplan for a building requires the creation of a two-dimensional floorplan from a 3D model. This task is widely employed in interior design and decoration. In reality, the structures of indoor environments are complex with much clutter and occlusions, making it difficult to reconstruct a complete and accurate floorplan. It is well known that a suitable dataset is a key point to drive an effective algorithm, while existing datasets of floorplan reconstruction are synthetic and small. Without reliable accumulations of real datasets, the robustness of methods to real scene reconstruction is weakened. In this paper, we first annotate a large-scale realistic benchmark, which contains RGBD image sequences and 3D models of 80 indoor scenes with more than 10,000 square meters. We also introduce a framework for the floorplan reconstruction with mesh-based point cloud normalization. The loose-Manhattan constraint is performed in our optimization process, and the optimal floorplan is reconstructed via constraint integer programming. The experimental results on public and our own datasets demonstrate that the proposed method outperforms FloorNet and Floor-SP.
AbstractList	The reconstruction of the floorplan for a building requires the creation of a two-dimensional floorplan from a 3D model. This task is widely employed in interior design and decoration. In reality, the structures of indoor environments are complex with much clutter and occlusions, making it difficult to reconstruct a complete and accurate floorplan. It is well known that a suitable dataset is a key point to drive an effective algorithm, while existing datasets of floorplan reconstruction are synthetic and small. Without reliable accumulations of real datasets, the robustness of methods to real scene reconstruction is weakened. In this paper, we first annotate a large-scale realistic benchmark, which contains RGBD image sequences and 3D models of 80 indoor scenes with more than 10,000 square meters. We also introduce a framework for the floorplan reconstruction with mesh-based point cloud normalization. The loose-Manhattan constraint is performed in our optimization process, and the optimal floorplan is reconstructed via constraint integer programming. The experimental results on public and our own datasets demonstrate that the proposed method outperforms FloorNet and Floor-SP.
Author	Xia, Wei Zhu, Zunjie Yan, Chenggang Wang, Qiting Chen, Ruolin
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SubjectTerms	Algorithms Architectural elements Architecture Automation Clutter data collection Datasets Deep learning Finite element method Flooring floorplan reconstruction Floorplans Image reconstruction Indoor environments Integer programming interior design Linear programming Manhattan Methods normalization Optimization planes point cloud Semantics Three dimensional models
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Title	Building Floorplan Reconstruction Based on Integer Linear Programming
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