Building component detection on unstructured 3d indoor point clouds using ransac-based region growing

Sangmin Oh, Dongmin Lee, Minju Kim, Taehoon Kim, Hunhee Cho

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)


With the advancement of light detection and ranging (LiDAR) technology, the mobile laser scanner (MLS) has been regarded as an important technology to collect geometric representations of the indoor environment. In particular, methods for detecting indoor objects from indoor point cloud data (PCD) captured through MLS have thus far been developed based on the trajectory of MLS. However, the existing methods have a limitation on applying to an indoor environment where the building components made by concrete impede obtaining the information of trajectory. Thus, this study aims to propose a building component detection algorithm for MLS-based indoor PCD without trajectory using random sample consensus (RANSAC)-based region growth. The proposed algorithm used the RANSAC and region growing to overcome the low accuracy and uniformity of MLS caused by the movement of LiDAR. This study ensures over 90% precision, recall, and proper segmentation rate of building component detection by testing the algorithm using the indoor PCD. The result of the case study shows that the proposed algorithm opens the possibility of accurately detecting interior objects from indoor PCD without trajectory information of MLS.

Original languageEnglish
Article number161
Pages (from-to)1-20
Number of pages20
JournalRemote Sensing
Issue number2
Publication statusPublished - 2021 Jan 2

Bibliographical note

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.


  • Building component detection
  • Indoor point cloud
  • Mobile laser scanner
  • Random sample consensus
  • Region growing

ASJC Scopus subject areas

  • General Earth and Planetary Sciences


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