Automated 3D volumetric reconstruction of multiple-room building interiors for as-built BIM

Jaehoon Jung, Cyrill Stachniss, Sungha Ju, Joon Heo

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)

Abstract

Currently, fully automated as-built modeling of building interiors using point-cloud data still remains an open challenge, due to several problems that repeatedly arise: (1) complex indoor environments containing multiple rooms; (2) time-consuming and labor-intensive noise filtering; (3) difficulties of representation of volumetric and detail-rich objects such as windows and doors. This study aimed to overcome such limitations while improving the amount of details reproduced within the model for further utilization in BIM. First, we input just the registered three-dimensional (3D) point-cloud data and segmented the point cloud into separate rooms for more effective performance of the later modeling phases for each room. For noise filtering, an offset space from the ceiling height was used to determine whether the scan points belonged to clutter or architectural components. The filtered points were projected onto a binary map in order to trace the floor-wall boundary, which was further refined through subsequent segmentation and regularization procedures. Then, the wall volumes were estimated in two ways: inside- and outside-wall-component modeling. Finally, the wall points were segmented and projected onto an inverse binary map, thereby enabling detection and modeling of the hollow areas as windows or doors. The experimental results on two real-world data sets demonstrated, through comparison with manually-generated models, the effectiveness of our approach: the calculated RMSEs of the two resulting models were 0.089 m and 0.074 m, respectively.

Original languageEnglish
Pages (from-to)811-825
Number of pages15
JournalAdvanced Engineering Informatics
Volume38
DOIs
Publication statusPublished - 2018 Oct

Bibliographical note

Funding Information:
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education ( 2015R1A6A3A03019594 ) and the Ministry of Science, ICT and Future Planning ( 2018R1A2B2009160 ).

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Artificial Intelligence

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