Abstract
Three-dimensional (3D) reconstruction of indoor environments from point cloud data is the process that has been studied and developed to facilitate the reconstruction of 3D models for old buildings. Prior to reconstruction, segmentation is the main process that is used to extract structural components such as floors, ceilings, and walls. This paper presents the segmentation method to extract the planar structures of a building from point cloud data. The original Random Sample Consensus (RANSAC) is modified by reducing computational complexity using localized sampling, and improving segmentation quality by adding smoothness constraints to the surface and applying the connectivity to the detected components. The results of the proposed method compared with those of the original RANSAC on the International Society for Photogrammetry and Remote Sensing (ISPRS) benchmark dataset indicate that the extracted components are more precise, more accurate, while also preserving the overall characteristics of the buildings.
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Acknowledgments
The authors would like to thank the Development and Promotion of Science and Technology Talents Project (DPST) for providing financial support. The implemented data were selected from the ISPRS benchmark on indoor modelling.
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Doougphummet, T., Boonserm, P., Lipikorn, R. (2023). 3D Building Internal Structural Component Segmentation from Point Cloud Data Using DBSCAN and Modified RANSAC with Normal Deviation Conditions. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Data Science and Algorithms in Systems. CoMeSySo 2022. Lecture Notes in Networks and Systems, vol 597. Springer, Cham. https://doi.org/10.1007/978-3-031-21438-7_7
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DOI: https://doi.org/10.1007/978-3-031-21438-7_7
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