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Reconstructing Floorplans from Point Clouds Using GAN.

Tianxing Jin^1,2, Jiayan Zhuang², Jiangjian Xiao²

¹Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China.

Journal of Imaging

|February 24, 2023

Summary

This study introduces a novel method for reconstructing floorplans from 3D indoor point clouds. It utilizes a generative adversarial network and edge optimization for accurate and efficient floorplan generation.

Keywords:

floorplan reconstruction generative adversarial network (GAN)indoor scene point clouds

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Area of Science:

Computer Vision
Artificial Intelligence
3D Reconstruction

Background:

Existing floorplan reconstruction methods often rely on geometric priors and complex optimization algorithms.
These methods can struggle with incomplete data and may not capture intricate structural details effectively.

Purpose of the Study:

To develop an efficient and accurate method for reconstructing detailed floorplans from indoor point clouds.
To reduce reliance on geometric priors and improve processing efficiency using deep learning.

Main Methods:

A generative adversarial network (GAN) was employed to learn indoor layout distributions and refine room segmentation masks.
An edge optimization algorithm was integrated to address pixel-level artifacts imperceptible to neural networks.
Constraint information of the overall layout was utilized for generating compact and semantically rich floorplans.

Main Results:

The proposed method demonstrated improved retention of shape information and enhanced accuracy of structural details.
Experimental results showed robustness and accuracy on complex 3D indoor datasets.
The algorithm achieved competitive performance compared to existing floorplan reconstruction methods.

Conclusions:

The developed method offers a more efficient and accurate approach to floorplan reconstruction from point clouds.
The integration of GANs and edge optimization successfully generates detailed and semantically meaningful floorplans.
This approach shows significant potential for applications in indoor mapping and virtual environment creation.