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Sensors 2017, 17(3), 621; doi:10.3390/s17030621

Implicit Regularization for Reconstructing 3D Building Rooftop Models Using Airborne LiDAR Data

Department of Earth and Space Science and Engineering, York University, 4700 Keele Street, Toronto M3J 1P3, ON, Canada
These authors contributed equally to this work.
Author to whom correspondence should be addressed.
Academic Editors: Changshan Wu and Shawn (Shixiong) Hu
Received: 22 January 2017 / Revised: 28 February 2017 / Accepted: 1 March 2017 / Published: 19 March 2017
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With rapid urbanization, highly accurate and semantically rich virtualization of building assets in 3D become more critical for supporting various applications, including urban planning, emergency response and location-based services. Many research efforts have been conducted to automatically reconstruct building models at city-scale from remotely sensed data. However, developing a fully-automated photogrammetric computer vision system enabling the massive generation of highly accurate building models still remains a challenging task. One the most challenging task for 3D building model reconstruction is to regularize the noises introduced in the boundary of building object retrieved from a raw data with lack of knowledge on its true shape. This paper proposes a data-driven modeling approach to reconstruct 3D rooftop models at city-scale from airborne laser scanning (ALS) data. The focus of the proposed method is to implicitly derive the shape regularity of 3D building rooftops from given noisy information of building boundary in a progressive manner. This study covers a full chain of 3D building modeling from low level processing to realistic 3D building rooftop modeling. In the element clustering step, building-labeled point clouds are clustered into homogeneous groups by applying height similarity and plane similarity. Based on segmented clusters, linear modeling cues including outer boundaries, intersection lines, and step lines are extracted. Topology elements among the modeling cues are recovered by the Binary Space Partitioning (BSP) technique. The regularity of the building rooftop model is achieved by an implicit regularization process in the framework of Minimum Description Length (MDL) combined with Hypothesize and Test (HAT). The parameters governing the MDL optimization are automatically estimated based on Min-Max optimization and Entropy-based weighting method. The performance of the proposed method is tested over the International Society for Photogrammetry and Remote Sensing (ISPRS) benchmark datasets. The results show that the proposed method can robustly produce accurate regularized 3D building rooftop models. View Full-Text
Keywords: 3D building rooftop modeling; building reconstruction; regularization; airborne laser scanning data; minimum description length 3D building rooftop modeling; building reconstruction; regularization; airborne laser scanning data; minimum description length

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Jung, J.; Jwa, Y.; Sohn, G. Implicit Regularization for Reconstructing 3D Building Rooftop Models Using Airborne LiDAR Data. Sensors 2017, 17, 621.

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