Automatic Room Segmentation of 3D Laser Data Using Morphological Processing

Jung, Jae Hwan; Stachniss, Cyrill; Kim, Changjae

doi:10.3390/ijgi6070206

Cited by 43 publications

(25 citation statements)

References 31 publications

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“…They employ an inside/outside labeling approach using binary linear programming. Jung et al [28] generate watertight floor maps by means of skeletonization in a 2D binary occupancy map with subse-quent labeling of separate rooms. A 3D room partitioning approach using anisotropic potential fields with subsequent unsupervised clustering has been presented by Bobkov et al [29].…”

Section: Abstraction Segmentation and Reconstructionmentioning

confidence: 99%

Automatic reconstruction of fully volumetric 3D building models from oriented point clouds

Ochmann

Vock

Klein

2019

ISPRS Journal of Photogrammetry and Remote Sensing

143

116

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A B S T R A C TWe present a novel method for reconstructing parametric, volumetric, multi-story building models from unstructured, unfiltered indoor point clouds by means of solving an integer linear optimization problem. Our approach overcomes limitations of previous methods in several ways: First, we drop assumptions about the input data such as the availability of separate scans as an initial room segmentation. Instead, a fully automatic room segmentation and outlier removal is performed on the unstructured point clouds. Second, restricting the solution space of our optimization approach to arrangements of volumetric wall entities representing the structure of a building enforces a consistent model of volumetric, interconnected walls fitted to the observed data instead of unconnected, paper-thin surfaces. Third, we formulate the optimization as an integer linear programming problem which allows for an exact solution instead of the approximations achieved with most previous techniques. Lastly, our optimization approach is designed to incorporate hard constraints which were difficult or even impossible to integrate before. We evaluate and demonstrate the capabilities of our proposed approach on a variety of complex real-world point clouds.

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Section: Abstraction Segmentation and Reconstructionmentioning

confidence: 99%

Automatic reconstruction of fully volumetric 3D building models from oriented point clouds

Ochmann

Vock

Klein

2019

ISPRS Journal of Photogrammetry and Remote Sensing

143

116

View full text Add to dashboard Cite

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“…However, detecting complete wall lines directly from the original point cloud is difficult because indoor environments exhibit extremely high levels of clutter and occlusion [1,15]. Moreover, certain portions of walls are not sampled because the sight of the laser scanner is occluded by clutter [1,36]. To ensure that almost all wall lines in the building can be detected, the points in each story are first split into several horizontal slices [1,42,52,53] that share the same floor plan structure, as shown in Figure 3a.…”

Section: Cell Decompositionmentioning

confidence: 99%

“…The DDP metric represents deviations between the selected corner points in the created floor map and reference data. This measure indicates the robustness against over-or under-segmentation [36]. The ADR metric represents deviations in rooms in the same location between the created floor map and ground-truth data.…”

Section: Input Datamentioning

confidence: 99%

Reconstruction of Three-Dimensional (3D) Indoor Interiors with Multiple Stories via Comprehensive Segmentation

Yang

et al. 2018

Remote Sensing

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Abstract:The fast and stable reconstruction of building interiors from scanned point clouds has recently attracted considerable research interest. However, reconstructing long corridors and connected areas across multiple floors has emerged as a substantial challenge. This paper presents a comprehensive segmentation method for reconstructing a three-dimensional (3D) indoor structure with multiple stories. With this method, the over-segmentation that usually occurs in the reconstruction of long corridors in a complex indoor environment is overcome by morphologically eroding the floor space to segment rooms and by overlapping the segmented room-space with partitioned cells via extracted wall lines. Such segmentation ensures both the integrity of the room-space partitions and the geometric regularity of the rooms. For spaces across floors in a multistory building, a peak-nadir-peak strategy in the distribution of points along the z-axis is proposed in order to extract connected areas across multiple floors. A series of experimental tests while using seven real-world 3D scans and eight synthetic models of indoor environments show the effectiveness and feasibility of the proposed method.

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“…More research focuses on room segmentation of the point cloud data without involving the trajectory, as the data is not necessarily able to be captured by a mobile laser scanner. Jung et al [19] proposed an automatic room segmentation method based on morphological image processing. The algorithms work on the 3D laser data from both terrestrial and mobile laser scanners.…”

Section: Related Workmentioning

confidence: 99%

“…Also, it refers to semantic place labeling in the robotics domain [17]. Several studies automated the process of room segmentation for domain-specific applications [18][19][20][21][22]. In the same context, the trajectory of the mobile laser scanner can reveal the topological relations between the spaces inside the building.…”

Section: Introductionmentioning

confidence: 99%

Space Subdivision of Indoor Mobile Laser Scanning Data Based on the Scanner Trajectory

Elseicy

Nikoohemat

Peter³

et al. 2018

Remote Sensing

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State-of-the-art indoor mobile laser scanners are now lightweight and portable enough to be carried by humans. They allow the user to map challenging environments such as multi-story buildings and staircases while continuously walking through the building. The trajectory of the laser scanner is usually discarded in the analysis, although it gives insight about indoor spaces and the topological relations between them. In this research, the trajectory is used in conjunction with the point cloud to subdivide the indoor space into stories, staircases, doorways, and rooms. Analyzing the scanner trajectory as a standalone dataset is used to identify the staircases and to separate the stories. Also, the doors that are traversed by the operator during the scanning are identified by processing only the interesting spots of the point cloud with the help of the trajectory. Semantic information like different space labels is assigned to the trajectory based on the detected doors. Finally, the point cloud is semantically enriched by transferring the labels from the annotated trajectory to the full point cloud. Four real-world datasets with a total of seven stories are used to evaluate the proposed methods. The evaluation items are the total number of correctly detected rooms, doors, and staircases.

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Automatic Room Segmentation of 3D Laser Data Using Morphological Processing

Cited by 43 publications

References 31 publications

Automatic reconstruction of fully volumetric 3D building models from oriented point clouds

Automatic reconstruction of fully volumetric 3D building models from oriented point clouds

Reconstruction of Three-Dimensional (3D) Indoor Interiors with Multiple Stories via Comprehensive Segmentation

Space Subdivision of Indoor Mobile Laser Scanning Data Based on the Scanner Trajectory

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