A Cost-Effective Method for Reconstructing City-Building 3D Models from Sparse Lidar Point Clouds

Kulawiak, Marek

doi:10.3390/rs14051278

Cited by 12 publications

(4 citation statements)

References 29 publications

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“…In recent years, reconstructing objects based on perspective images (i.e., photographs) has become increasingly popular, and various methods for achieving such reconstruction have been proposed. By using multiple photographs of an architectural object and knowing the position of the camera lens while shooting, it is possible to reconstruct the object accurately (Kulawiak, 2022;Ozimek et al, 2021;Harshit et al, 2022].…”

Section: Reconstruction Approachmentioning

confidence: 99%

Reconstruction of Architectural Heritage With Symmetrical Components

Jain,

Zlatanova,

Halder

2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Data capturing through either Lidar or photogrammetry, often results in incomplete and partial information related to a surface due to occlusion or inaccessibility of the clear object vision. In case of asymmetrical objects yet the reconstruction is unattainable by any means, meanwhile the approach for the development of the missing information could be done in cases of symmetrical objects. In this paper we have advised a semi-automatic approach for recreating missing or incomplete information from the partially captured data using space sub-division and 3D transformation. The study has been done on a 175 year-old building whose scanned information is available for only one side and captures a façade with four columns. The idea is to first extract the symmetrical parts through segmentation of different building parts. Then the columns with partial information have been oriented as per a reference plane based on the pose and centre computed from the horizontal parts. The instance is then used to fill in the lost information through duplication and transformation. This approach can be used to recreate structures with symmetrical elements, which are partially destroyed from withering, disaster, or any human intervention.

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Section: Reconstruction Approachmentioning

confidence: 99%

Reconstruction of Architectural Heritage With Symmetrical Components

Jain,

Zlatanova,

Halder

2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Light detection and ranging (LiDAR) technology, which collects point clouds using airborne laser Scanning (ALS), has particularly contributed to advancements in remote measurement. Airborne LiDAR data are described by three coordinates (attributes), which, in combination with aerial color images (red, green, and blue; RGB), have led to the development of a new functionality in 3D modeling [1][2][3]. LiDAR data can be labeled by automatic point classification, where thematic subsets are created based on attributes [2,4,5].…”

Section: Introductionmentioning

confidence: 99%

3D LoD2 and LoD3 Modelling of Rotating Surface Building of Ornamental Towers starting from LiDAR Data

Lewandowicz,

Kurdi,

Gharineiat

2023

Prime Archives in Remote Sensing

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This paper presents an innovative approach to the automatic modeling of buildings composed of rotational surfaces, based exclusively on airborne LiDAR point clouds. The proposed approach starts by detecting the gravity center of the building's footprint. A thin point slice parallel to one coordinate axis around the gravity center was considered, and a vertical crosssection was rotated around a vertical axis passing through the gravity center, to generate the 3D building model. The constructed model was visualized with a matrix composed of three matrices, where the same dimensions represented the X, Y, and Z Euclidean coordinates. Five tower point clouds were used www.videleaf.com to evaluate the performance of the proposed algorithm. Then, to estimate the accuracy, the point cloud was superimposed onto the constructed model, and the deviation of points describing the building model was calculated, in addition to the standard deviation. The obtained standard deviation values, which express the accuracy, were determined in the range of 0.21 m to 1.41 m. These values indicate that the accuracy of the suggested method is consistent with approaches suggested previously in the literature. In the future, the obtained model could be enhanced with the use of points that have considerable deviations. The applied matrix not only facilitates the modeling of buildings with various levels of architectural complexity, but it also allows for local enhancement of the constructed models.

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Section: Introductionmentioning

confidence: 99%

3D LoD2 and LoD3 Modeling of Buildings with Ornamental Towers and Turrets Based on LiDAR Data

Lewandowicz¹,

Kurdi

Gharineiat

2022

Remote Sensing

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This paper presents an innovative approach to the automatic modeling of buildings composed of rotational surfaces, based exclusively on airborne LiDAR point clouds. The proposed approach starts by detecting the gravity center of the building's footprint. A thin point slice parallel to one coordinate axis around the gravity center was considered, and a vertical cross-section was rotated around a vertical axis passing through the gravity center, to generate the 3D building model. The constructed model was visualized with a matrix composed of three matrices, where the same dimensions represented the X, Y, and Z Euclidean coordinates. Five tower point clouds were used to evaluate the performance of the proposed algorithm. Then, to estimate the accuracy, the point cloud was superimposed onto the constructed model, and the deviation of points describing the building model was calculated, in addition to the standard deviation. The obtained standard deviation values, which express the accuracy, were determined in the range of 0.21 m to 1.41 m. These values indicate that the accuracy of the suggested method is consistent with approaches suggested previously in the literature. In the future, the obtained model could be enhanced with the use of points that have considerable deviations. The applied matrix not only facilitates the modeling of buildings with various levels of architectural complexity, but it also allows for local enhancement of the constructed models.

show abstract

A Cost-Effective Method for Reconstructing City-Building 3D Models from Sparse Lidar Point Clouds

Cited by 12 publications

References 29 publications

Reconstruction of Architectural Heritage With Symmetrical Components

Reconstruction of Architectural Heritage With Symmetrical Components

3D LoD2 and LoD3 Modelling of Rotating Surface Building of Ornamental Towers starting from LiDAR Data

3D LoD2 and LoD3 Modeling of Buildings with Ornamental Towers and Turrets Based on LiDAR Data

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