A Triangulation-based Technique for Building Boundary Identification from Point Cloud Data

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Author(s)
Awrangjeb, Mohammad
Lu, Guojun
Griffith University Author(s)
Year published
2015
Metadata
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Building boundary identification is an essential prerequisite in building outline generation from point cloud data. In this problem, boundary edges that constitute the building boundary are identified. The existing solutions to the identification of boundary edges from the input point set have one or more of the following problems: ineffective in finding appropriate edges in a concave shape, incapable of determining a `hole' or `concavity' inside the shape separately, dependant on additional information such as the scan direction that may be unavailable, and incompetent in determining the boundary of a point set from the ...
View more >Building boundary identification is an essential prerequisite in building outline generation from point cloud data. In this problem, boundary edges that constitute the building boundary are identified. The existing solutions to the identification of boundary edges from the input point set have one or more of the following problems: ineffective in finding appropriate edges in a concave shape, incapable of determining a `hole' or `concavity' inside the shape separately, dependant on additional information such as the scan direction that may be unavailable, and incompetent in determining the boundary of a point set from the boundaries of two or more subsets of the point set. This paper proposes a new solution to the identification of building boundary by using the maximum point-to-point distance in the input data. It properly detects the boundary edges for any type of shape and separately recognises holes, if any, inside the shape. The unique feature of the proposed solution is that it can identify the boundary of a point set from the boundaries of two or more subsets of the point set. It does not require any additional information other than the input point set. Experimental results show that the proposed solution can preserve details along the building boundary and offer high area-based completeness and quality, even in low density input data.
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View more >Building boundary identification is an essential prerequisite in building outline generation from point cloud data. In this problem, boundary edges that constitute the building boundary are identified. The existing solutions to the identification of boundary edges from the input point set have one or more of the following problems: ineffective in finding appropriate edges in a concave shape, incapable of determining a `hole' or `concavity' inside the shape separately, dependant on additional information such as the scan direction that may be unavailable, and incompetent in determining the boundary of a point set from the boundaries of two or more subsets of the point set. This paper proposes a new solution to the identification of building boundary by using the maximum point-to-point distance in the input data. It properly detects the boundary edges for any type of shape and separately recognises holes, if any, inside the shape. The unique feature of the proposed solution is that it can identify the boundary of a point set from the boundaries of two or more subsets of the point set. It does not require any additional information other than the input point set. Experimental results show that the proposed solution can preserve details along the building boundary and offer high area-based completeness and quality, even in low density input data.
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Conference Title
2015 INTERNATIONAL CONFERENCE ON IMAGE AND VISION COMPUTING NEW ZEALAND (IVCNZ)
Volume
2016-November
Copyright Statement
© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Subject
Image Processing
Computer Vision
Photogrammetry and Remote Sensing