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Erratum: Zhai, J., et al. Quality Assessment Method for Linear Feature Simplification Based on Multi-Scale Spatial Uncertainty. ISPRS International Journal of Geo-Information 2017, 6, 184
Open AccessArticle

Automatic Room Segmentation of 3D Laser Data Using Morphological Processing

School of Civil and Construction Engineering, Oregon State University, 1691 SW Campus Way, Corvallis, OR 97331, USA
Department of Photogrammetry, University of Bonn, Nussallee 15, 53115 Bonn, Germany
Department of Civil and Environmental Engineering, Myongji University, 116 Myongji-ro, Cheoin-gu, Yongin, Gyeonggi-do 449-728, Korea
Author to whom correspondence should be addressed.
Academic Editor: Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2017, 6(7), 206;
Received: 11 May 2017 / Revised: 3 July 2017 / Accepted: 4 July 2017 / Published: 7 July 2017
In this paper, we introduce an automatic room segmentation approach based on morphological processing. The inputs are registered point-clouds obtained from either a static laser scanner or a mobile scanning system, without any required prior information or initial labeling satisfying specific conditions. The proposed segmentation method’s main concept, based on the assumption that each room is bound by vertical walls, is to project the 3D point cloud onto a 2D binary map and to close all openings (e.g., doorways) to other rooms. This is achieved by creating an initial segment map, skeletonizing the surrounding walls of each segment, and iteratively connecting the closest pixels between the skeletonized walls. By iterating this procedure for all initial segments, the algorithm produces a “watertight” floor map, on which each room can be segmented by a labeling process. Finally, the original 3D points are segmented according to their 2D locations as projected on the segment map. The novel features of our approach are: (1) its robustness against occlusions and clutter in point-cloud input; (2) high segmentation performance regardless of the number of rooms or architectural complexity; and (3) straight segmentation boundary generation, all of which were proved in experiments with various sets of real-world, synthetic, and publicly available data. Additionally, comparisons with the five popular existing methods through both qualitative and quantitative evaluations demonstrated the feasibility of the proposed approach. View Full-Text
Keywords: point cloud; room segmentation; morphological processing; as-built BIM point cloud; room segmentation; morphological processing; as-built BIM
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Jung, J.; Stachniss, C.; Kim, C. Automatic Room Segmentation of 3D Laser Data Using Morphological Processing. ISPRS Int. J. Geo-Inf. 2017, 6, 206.

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