Automatic Reconstruction of Multi-Level Indoor Spaces from Point Cloud and Trajectory
Remarkable progress in the development of modeling methods for indoor spaces has been made in recent years with a focus on the reconstruction of complex environments, such as multi-room and multi-level buildings. Existing methods represent indoor structure models as a combination of several sub-spac...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-05-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/10/3493 |
| _version_ | 1827692255721291776 |
|---|---|
| author | Gahyeon Lim Nakju Doh |
| author_facet | Gahyeon Lim Nakju Doh |
| author_sort | Gahyeon Lim |
| collection | DOAJ |
| description | Remarkable progress in the development of modeling methods for indoor spaces has been made in recent years with a focus on the reconstruction of complex environments, such as multi-room and multi-level buildings. Existing methods represent indoor structure models as a combination of several sub-spaces, which are constructed by room segmentation or horizontal slicing approach that divide the multi-room or multi-level building environments into several segments. In this study, we propose an automatic reconstruction method of multi-level indoor spaces with unique models, including inter-room and inter-floor connections from point cloud and trajectory. We construct structural points from registered point cloud and extract piece-wise planar segments from the structural points. Then, a three-dimensional space decomposition is conducted and water-tight meshes are generated with energy minimization using graph cut algorithm. The data term of the energy function is expressed as a difference in visibility between each decomposed space and trajectory. The proposed method allows modeling of indoor spaces in complex environments, such as multi-room, room-less, and multi-level buildings. The performance of the proposed approach is evaluated for seven indoor space datasets. |
| first_indexed | 2024-03-10T11:19:43Z |
| format | Article |
| id | doaj.art-1763ecc516df4940a33b105bb5235a93 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T11:19:43Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-1763ecc516df4940a33b105bb5235a932023-11-21T20:07:22ZengMDPI AGSensors1424-82202021-05-012110349310.3390/s21103493Automatic Reconstruction of Multi-Level Indoor Spaces from Point Cloud and TrajectoryGahyeon Lim0Nakju Doh1School of Electrical Engineering, Korea University, Seoul 02841, KoreaTeeLabs, Seoul 02857, KoreaRemarkable progress in the development of modeling methods for indoor spaces has been made in recent years with a focus on the reconstruction of complex environments, such as multi-room and multi-level buildings. Existing methods represent indoor structure models as a combination of several sub-spaces, which are constructed by room segmentation or horizontal slicing approach that divide the multi-room or multi-level building environments into several segments. In this study, we propose an automatic reconstruction method of multi-level indoor spaces with unique models, including inter-room and inter-floor connections from point cloud and trajectory. We construct structural points from registered point cloud and extract piece-wise planar segments from the structural points. Then, a three-dimensional space decomposition is conducted and water-tight meshes are generated with energy minimization using graph cut algorithm. The data term of the energy function is expressed as a difference in visibility between each decomposed space and trajectory. The proposed method allows modeling of indoor spaces in complex environments, such as multi-room, room-less, and multi-level buildings. The performance of the proposed approach is evaluated for seven indoor space datasets.https://www.mdpi.com/1424-8220/21/10/3493automatic 3D modelingstructured 3D reconstructionmulti-level building reconstructionpoint cloud processing |
| spellingShingle | Gahyeon Lim Nakju Doh Automatic Reconstruction of Multi-Level Indoor Spaces from Point Cloud and Trajectory Sensors automatic 3D modeling structured 3D reconstruction multi-level building reconstruction point cloud processing |
| title | Automatic Reconstruction of Multi-Level Indoor Spaces from Point Cloud and Trajectory |
| title_full | Automatic Reconstruction of Multi-Level Indoor Spaces from Point Cloud and Trajectory |
| title_fullStr | Automatic Reconstruction of Multi-Level Indoor Spaces from Point Cloud and Trajectory |
| title_full_unstemmed | Automatic Reconstruction of Multi-Level Indoor Spaces from Point Cloud and Trajectory |
| title_short | Automatic Reconstruction of Multi-Level Indoor Spaces from Point Cloud and Trajectory |
| title_sort | automatic reconstruction of multi level indoor spaces from point cloud and trajectory |
| topic | automatic 3D modeling structured 3D reconstruction multi-level building reconstruction point cloud processing |
| url | https://www.mdpi.com/1424-8220/21/10/3493 |
| work_keys_str_mv | AT gahyeonlim automaticreconstructionofmultilevelindoorspacesfrompointcloudandtrajectory AT nakjudoh automaticreconstructionofmultilevelindoorspacesfrompointcloudandtrajectory |