Façade Separation in Ground-Based LiDAR Point Clouds Based on Edges and Windows

Xia, Shaobo; Wang, Rui-Sheng

doi:10.1109/jstars.2019.2897987

Cited by 27 publications

(18 citation statements)

References 44 publications

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“…In complex indoor scenes, the efficiency of surface extraction is low and contains excessive noise. Line-based reconstruction methods [32][33][34][35][36][37][38][39][40] can be used to completely represent the geometric information; however, these do not contain semantic information and adjacency relationship of rooms, leaving the reconstructed models to be useful only for visualization. In order to address the above shortcomings, we propose an innovative approach combining the rich structure of 2D lines with 3D geometry of surfaces to automatically build 3D structured models using MLS point cloud data.…”

Section: Discussionmentioning

confidence: 99%

“…Current methods for the reconstruction of indoor spaces are mainly based on the extraction of surfaces [9,15,[22][23][24][25][26][27][28][29][30][31] and lines [32][33][34][35][36][37][38][39][40].…”

Section: Reconstruction Of Indoor Spacementioning

confidence: 99%

“…However, the line elements remained completely isolated and devoid of information about topological relations. Similarly, Xia and Wang [33], Lu et al [34] extracted unstructured line elements from point clouds. Liu et al [35] proposed the FloorNet where a deep neural architecture can automatically reconstruct the floorplan from RGBD videos with camera poses.…”

Section: Reconstruction Of Indoor Spacementioning

confidence: 99%

“…Large scale scene modelling based on surface elements has significant computational challenges. In order to increase computational efficiency, many line-based reconstructed methods have been proposed [32][33][34][35][36][37][38][39][40]. Their results have shown that line-based methods are efficient and effective, which can produce accurate and complete line segments.…”

Section: Introductionmentioning

confidence: 99%

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Structural 3D Reconstruction of Indoor Space for 5G Signal Simulation with Mobile Laser Scanning Point Clouds

Cui

Dong

2019

Remote Sensing

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3D modelling of indoor environment is essential in smart city applications such as building information modelling (BIM), spatial location application, energy consumption estimation, and signal simulation, etc. Fast and stable reconstruction of 3D models from point clouds has already attracted considerable research interest. However, in the complex indoor environment, automated reconstruction of detailed 3D models still remains a serious challenge. To address these issues, this paper presents a novel method that couples linear structures with three-dimensional geometric surfaces to automatically reconstruct 3D models using point cloud data from mobile laser scanning. In our proposed approach, a fully automatic room segmentation is performed on the unstructured point clouds via multi-label graph cuts with semantic constraints, which can overcome the over-segmentation in the long corridor. Then, the horizontal slices of point clouds with individual room are projected onto the plane to form a binary image, which is followed by line extraction and regularization to generate floorplan lines. The 3D structured models are reconstructed by multi-label graph cuts, which is designed to combine segmented room, line and surface elements as semantic constraints. Finally, this paper proposed a novel application that 5G signal simulation based on the output structural model to aim at determining the optimal location of 5G small base station in a large-scale indoor scene for the future. Four datasets collected using handheld and backpack laser scanning systems in different locations were used to evaluate the proposed method. The results indicate our proposed methodology provides an accurate and efficient reconstruction of detailed structured models from complex indoor scenes.

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Section: Discussionmentioning

confidence: 99%

Section: Reconstruction Of Indoor Spacementioning

confidence: 99%

Section: Reconstruction Of Indoor Spacementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Structural 3D Reconstruction of Indoor Space for 5G Signal Simulation with Mobile Laser Scanning Point Clouds

Cui

Dong

2019

Remote Sensing

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“…Previous research on this topic has segmented individual façades from terrestrial point clouds for feature extraction to support urban modeling. Xia and Wang (2019) extracted edges and windows from point cloud façade models with complex features. They defined an objective function that considers both window edge intersections and their vertical elevations on the building face.…”

Section: Introductionmentioning

confidence: 99%

Creation, transformation, and orientation adjustment of a building façade model for feature segmentation : transforming 3D building point cloud models into 2D georeferenced feature overlays

Blundell¹,

Devine²

2020

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