Reconstruction of LoD-2 Building Models Guided by Façade Structures from Oblique Photogrammetric Point Cloud

Wang, Feng; Zhou, Guoqing; Hu, Han; Wang, Yuefeng; Fu, Bolin; Li, Shiming; Xie, Jiali

doi:10.3390/rs15020400

Cited by 14 publications

(7 citation statements)

References 38 publications

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“…Substituting Equation (37) in Equation (36), Equation ( 38) is derived for the calculation of the dip angle. In the right triangle OCK 1 , Equation ( 37) is derived.…”

Section: Dip Anglementioning

confidence: 99%

“…Substituting Equation (37) in Equation (36), Equation ( 38) is derived for the calculation of the dip angle.…”

Section: Dip Anglementioning

confidence: 99%

“…Both the scientific community and multiple companies have been utilizing oblique images for diverse applications, leading to significant advancements in their automated processing [2,3]. Applications utilizing oblique aerial images include-but are not limited to-image matching [4][5][6], georeferencing [3,[7][8][9], orientation and structure from motion procedures [10][11][12][13][14][15], multi-view stereo and 3D modeling pipelines [16][17][18][19][20], texture mapping [21][22][23], object detection [24,25], building identification [26][27][28], semantic segmentation of 3D city models [29] and buildings [30,31], building classification [32], extraction of post-disaster structural damages [33][34][35], historic building information modeling (HBIM) [36], reconstruction of LoD-2 building models [37], cadastral mapping [38], 3D reconstruction of canopy [39] and estimation of canopy height [40], moving car recognition [41], animal detection [42], and river surface ice quantification …”

Section: Introductionmentioning

confidence: 99%

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Oblique Aerial Images: Geometric Principles, Relationships and Definitions

Verykokou,

Ioannidis

2024

Encyclopedia

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Aerial images captured with the camera optical axis deliberately inclined with respect to the vertical are defined as oblique aerial images. Throughout the evolution of aerial photography, oblique aerial images have held a prominent place since its inception. While vertical airborne images dominated in photogrammetric applications for over a century, the advancements in photogrammetry and computer vision algorithms, coupled with the growing accessibility of oblique images in the market, have propelled the rise of oblique images in recent times. Their emergence is attributed to inherent advantages they offer over vertical images. In this entry, basic definitions, geometric principles and relationships for oblique aerial images, necessary for understanding their underlying geometry, are presented.

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“…Substituting Equation (37) in Equation (36), Equation ( 38) is derived for the calculation of the dip angle. In the right triangle OCK 1 , Equation ( 37) is derived.…”

Section: Dip Anglementioning

confidence: 99%

“…Substituting Equation (37) in Equation (36), Equation ( 38) is derived for the calculation of the dip angle.…”

Section: Dip Anglementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Oblique Aerial Images: Geometric Principles, Relationships and Definitions

Verykokou,

Ioannidis

2024

Encyclopedia

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“…First, the contour-based line extraction method [36,37] is used to extract line features from the oblique photogrammetric surface mesh and 3D laser point cloud. For the photogrammetric mesh model, the relative ground elevation h s of the cutting plane is given along the Z axis direction.…”

Section: Line Feature Extractionmentioning

confidence: 99%

Geometry and Topology Reconstruction of BIM Wall Objects from Photogrammetric Meshes and Laser Point Clouds

et al. 2023

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As the foundation for digitalization, building information modeling (BIM) technology has been widely used in the field of architecture, engineering, construction, and facility management (AEC/FM). Unmanned aerial vehicle (UAV) oblique photogrammetry and laser scanning have become increasingly popular data acquisition techniques for surveying buildings and providing original data for BIM modeling. However, the geometric and topological reconstruction of solid walls, which are among the most important architectural structures in BIM, is still a challenging undertaking. Due to noise and missing data in 3D point clouds, current research mostly focuses on segmenting wall planar surfaces from unstructured 3D point clouds and fitting the plane parameters without considering the thickness or 3D shape of the wall. Point clouds acquired only from the indoor space are insufficient for modeling exterior walls. It is also important to maintain the topological relationships between wall objects to meet the needs of complex BIM modeling. Therefore, in this study, a geometry and topology modeling method is proposed for solid walls in BIM based on photogrammetric meshes and laser point clouds. The method uses a kinetic space-partitioning algorithm to generate the building footprint and indoor floor plan. It classifies interior and exterior wall segments and infers parallel line segments to extract wall centerlines. The topological relationships are reconstructed and maintained to build wall objects with consistency. Experimental results on two datasets, including both photogrammetric meshes and indoor laser point clouds, exhibit more than 90% completeness and correctness, as well as centimeter-level accuracy of the wall surfaces.

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“…Point clouds are used extensively in remote sensing fields such as classification [1,2], building reconstruction [3][4][5][6], and autonomous driving [7,8]. In recent years, thanks to the development of deep neural networks, the research on point clouds has been greatly accelerated.…”

Section: Introductionmentioning

confidence: 99%

PU-CTG: A Point Cloud Upsampling Network Using Transformer Fusion and GRU Correction

Li,

Lin,

Cheng

et al. 2024

Remote Sensing

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Point clouds are widely used in remote sensing applications, e.g., 3D object classification, semantic segmentation, and building reconstruction. Generating dense and uniformly distributed point clouds from low-density ones is beneficial to 3D point cloud applications. The traditional methods mainly focus on the global shape of 3D point clouds, thus ignoring detailed representations. The enhancement of detailed features is conducive to generating dense and uniform point clouds. In this paper, we propose a point cloud upsampling network to improve the detail construction ability, named PU-CTG. The proposed method is implemented by a cross-transformer-fused module and a GRU-corrected module. The aim of the cross-transformer module is to enable the interaction and effective fusion between different scales of features so that the network can capture finer features. The purpose of the gated recurrent unit (GRU) is to reconstruct fine-grained features by rectifying the feedback error. The experimental results demonstrate the effectiveness of our method. Furthermore, the ModelNet40 dataset is upsampled by PU-CTG, and the classification experiment is applied to PointNet to verify the promotion ability of this network.

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Reconstruction of LoD-2 Building Models Guided by Façade Structures from Oblique Photogrammetric Point Cloud

Cited by 14 publications

References 38 publications

Oblique Aerial Images: Geometric Principles, Relationships and Definitions

Oblique Aerial Images: Geometric Principles, Relationships and Definitions

Geometry and Topology Reconstruction of BIM Wall Objects from Photogrammetric Meshes and Laser Point Clouds

PU-CTG: A Point Cloud Upsampling Network Using Transformer Fusion and GRU Correction

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