FAIS: Fully Automatic Indoor Surveying Framework of Terrestrial Laser Scanning Point Clouds in Large-Scale Indoor Environments

This article presents a novel fully automatic indoor surveying (FAIS) framework for large-scale indoor environments using a Terrestrial Laser Scanning (TLS) hardware system. Traditional methods for indoor surveying are labor-intensive and time-consuming, as they rely on manually positioning scanners for data capture and placing markers for registration. What is more, positioning scanners manually may cause uneven scanning or rescanning, including unstructured areas specifically. To ensure full coverage of the scene, we precisely obtain the number and location of scan stations through the Signed Distance Function (SDF) based method. Meanwhile, we propose an efficient large-scale dense point cloud registration method without markers. The proposed framework is adapted to environments where the scanner operates on a flat surface, such as office spaces, theater stage spaces, urban areas, and some cultural heritage scenic areas. Experiments demonstrate that the proposed method decreases computation time and obtains a more complete point cloud.

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Publication Details

Journal
Remote Sensing
Published
2025-08-17
DOI
https://doi.org/10.3390/rs17162863
Primary Topic
3D Surveying and Cultural Heritage
Type
article
Field-Weighted Citation Impact
0.00

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article

FAIS: Fully Automatic Indoor Surveying Framework of Terrestrial Laser Scanning Point Clouds in Large-Scale Indoor Environments

Shiyi Guo, Wenhao Li, Yizhe Liu, Tong Jia et al.
Remote Sensing
3D Surveying and Cultural Heritage
article

FAIS: Fully Automatic Indoor Surveying Framework of Terrestrial Laser Scanning Point Clouds in Large-Scale Indoor Environments

Shiyi Guo, Wenhao Li, Yizhe Liu, Tong Jia, Hao Wang, Yunchun Zhou
article en

Abstract

This article presents a novel fully automatic indoor surveying (FAIS) framework for large-scale indoor environments using a Terrestrial Laser Scanning (TLS) hardware system. Traditional methods for indoor surveying are labor-intensive and time-consuming, as they rely on manually positioning scanners for data capture and placing markers for registration. What is more, positioning scanners manually may cause uneven scanning or rescanning, including unstructured areas specifically. To ensure full coverage of the scene, we precisely obtain the number and location of scan stations through the Signed Distance Function (SDF) based method. Meanwhile, we propose an efficient large-scale dense point cloud registration method without markers. The proposed framework is adapted to environments where the scanner operates on a flat surface, such as office spaces, theater stage spaces, urban areas, and some cultural heritage scenic areas. Experiments demonstrate that the proposed method decreases computation time and obtains a more complete point cloud.

Remote SensingVol. 17(16)
Northeastern University (CN)
National Natural Science Foundation of China, Higher Education Discipline Innovation Project, Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 71%
3D Surveying and Cultural Heritage
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FAIS: Fully Automatic Indoor Surveying Framework of Terrestrial Laser Scanning Point Clouds in Large-Scale Indoor Environments — Shiyi Guo, Wenhao Li, et al. · Remote Sensing (2025) | TGRS Research Map | TGRS