Three-Dimensional Wireframe Reconstruction for Non-Manhattan-Shaped Point Clouds
Publication: Journal of Computing in Civil Engineering
Volume 38, Issue 6
Abstract
This study proposes a feature relationship algorithm (FRA) to reconstruct three-dimensional wireframes of objects with non-Manhattan shapes using segmented point clouds. Instead of relying on extracting target boundaries, the FRA systematically identifies the vertex and edge nodes of objects and uses an innovative linking strategy to reconstruct a precise wireframe based on the point cloud geometry, even in the presence of data gaps. The FRA exhibits adaptability to various shapes, including curves, cones, pyramids, cylinders, octagonal prisms, and combinations. Validations on synthetic data provide valuable insights into the FRA’s parameter tuning and exceptional shape accuracy. On the other hand, the use of Light Detection and Ranging scans and benchmark data underscores the FRA’s fidelity in representing shapes from point clouds and demonstrates its improvement over baseline methods.
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Data Availability Statement
Some data, models, or codes supporting this study’s findings are available from the corresponding author upon reasonable request.
Acknowledgments
This publication would not have been possible without constructive suggestions from reviewers, which are much appreciated. This research is supported by the National Science and Technology Council of Taiwan under Grant 111-2221-E-A49 -036 -.
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Published In
Journal of Computing in Civil Engineering
Volume 38 • Issue 6 • November 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Jan 23, 2024
Accepted: Jun 11, 2024
Published online: Sep 11, 2024
Published in print: Nov 1, 2024
Discussion open until: Feb 11, 2025
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