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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Vydané v:Remote sensing (Basel, Switzerland) Ročník 14; číslo 18; s. 4675
Hlavní autori: Wang, Qiting, Zhu, Zunjie, Chen, Ruolin, Xia, Wei, Yan, Chenggang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.09.2022
Predmet:
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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Shrnutí: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.
Bibliografia:ObjectType-Article-1
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ISSN:2072-4292
2072-4292
DOI:10.3390/rs14184675