Room segmentation in 3D point clouds using anisotropic potential fields

Bobkov, Dmytro; Kiechle, Martina; Hilsenbeck, Sebastian; Steinbach, Eckehard

doi:10.1109/icme.2017.8019484

Cited by 12 publications

(13 citation statements)

References 14 publications

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“…Secondly, the reasoning and iterative classification process build rooms according to the links between walls, ceilings and floors. The comparison of this approach with References [28,29] approaches on the same data allow the illustration of its benefits. Reference [48] provides the data used for the comparison.…”

Section: Discussionmentioning

confidence: 99%

“…Other approaches in References [26,27] combine 2D and 3D data to increase detection quality. Among the machine learning approaches, the approaches of References [28,29] obtain excellent results in the application case of room detection in a 3D modern building. That is why Section 4 uses the result of their works to compare with the result of the approach presented in this paper.…”

Section: Related Workmentioning

confidence: 99%

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Automatic Detection of Objects in 3D Point Clouds Based on Exclusively Semantic Guided Processes

Ponciano

Trémeau

Boochs

2019

IJGI

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In the domain of computer vision, object recognition aims at detecting and classifying objects in data sets. Model-driven approaches are typically constrained through their focus on either a specific type of data, a context (indoor, outdoor) or a set of objects. Machine learning-based approaches are more flexible but also constrained as they need annotated data sets to train the learning process. That leads to problems when this data is not available through the specialty of the application field, like archaeology, for example. In order to overcome such constraints, we present a fully semantic-guided approach. The role of semantics is to express all relevant knowledge of the representation of the objects inside the data sets and of the algorithms which address this representation. In addition, the approach contains a learning stage since it adapts the processing according to the diversity of the objects and data characteristics. The semantic is expressed via an ontological model and uses standard web technology like SPARQL queries, providing great flexibility. The ontological model describes the object, the data and the algorithms. It allows the selection and execution of algorithms adapted to the data and objects dynamically. Similarly, processing results are dynamically classified and allow for enriching the ontological model using SPARQL construct queries. The semantic formulated through SPARQL also acts as a bridge between the knowledge contained within the ontological model and the processing branch, which executes algorithms. It provides the capability to adapt the sequence of algorithms to an individual state of the processing chain and makes the solution robust and flexible. The comparison of this approach with others on the same use case shows the efficiency and improvement this approach brings.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Automatic Detection of Objects in 3D Point Clouds Based on Exclusively Semantic Guided Processes

Ponciano

Trémeau

Boochs

2019

IJGI

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“…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]. Pursuing a similar goal, Ochmann et al [30] perform a segmentation of indoor point clouds into separate rooms using a visibility-based approach.…”

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

145

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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“…Finally, an energy minimization algorithm is applied for semantic labeling. Bobkov et al [29] proposed an automatic approach for room segmentation for the point cloud data. The approach makes use of interior free space to identify the rooms.…”

Section: Related Workmentioning

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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Room segmentation in 3D point clouds using anisotropic potential fields

Cited by 12 publications

References 14 publications

Automatic Detection of Objects in 3D Point Clouds Based on Exclusively Semantic Guided Processes

Automatic Detection of Objects in 3D Point Clouds Based on Exclusively Semantic Guided Processes

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

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

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