LiDAR Point Clouds to 3-D Urban Models$:$ A Review

Wang, Ruisheng; Peethambaran, Jiju; Chen, Dong

doi:10.1109/jstars.2017.2781132

Cited by 212 publications

(114 citation statements)

References 162 publications

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“…Comprehensive reviews of the recent developments in processing of point clouds acquired from construction sites and indoor environments can be found in [8][9][10][11][12][13][14]. Since the focus of this manuscript is the automated semantic extraction of structural components in regular rectangular concrete construction, the review of previous work is restricted to that addressing the specific problem of automated semantic labeling of objects with predominantly planar and linear facades.…”

Section: State Of the Art In Semantic Extraction Of Structural Componmentioning

confidence: 99%

Automatic Recognition of Common Structural Elements from Point Clouds for Automated Progress Monitoring and Dimensional Quality Control in Reinforced Concrete Construction

2019

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This manuscript provides a robust framework for the extraction of common structural components, such as columns, from terrestrial laser scanning point clouds acquired at regular rectangular concrete construction projects. The proposed framework utilizes geometric primitive as well as relationship-based reasoning between objects to semantically label point clouds. The framework then compares the extracted objects to the planned building information model (BIM) to automatically identify the as-built schedule and dimensional discrepancies. A novel method was also developed to remove redundant points of a newly acquired scan to detect changes between consecutive scans independent of the planned BIM. Five sets of point cloud data were acquired from the same construction site at different time intervals to assess the effectiveness of the proposed framework. In all datasets, the framework successfully extracted 132 out of 133 columns and achieved an accuracy of 98.79% for removing redundant surfaces. The framework successfully determined the progress of concrete work at each epoch in both activity and project levels through earned value analysis. It was also shown that the dimensions of 127 out of the 132 columns and all the slabs complied with those in the planned BIM.

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Section: State Of the Art In Semantic Extraction Of Structural Componmentioning

confidence: 99%

Automatic Recognition of Common Structural Elements from Point Clouds for Automated Progress Monitoring and Dimensional Quality Control in Reinforced Concrete Construction

2019

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“…Hybrid modelling often uses the ground plans or sets of nonoverlapped and connected quadrilaterals to divide the complex rooftop into subsets where the solution already exists." (Wang et al, 2018) Case-studies using the ISPRS benchmark (Rottensteiner et al, 2014) have disseminated data only for regular buildings with well-known typo-morphology; however, there is a lack of studies with agglomerated buildings. Obtaining the agglomerated buildings complexity in aerial data (photo and Lidar) is intricate, mainly because of no splitting into single buildings and because the ground is almost invisible by aerial view; consequently, no regular models can be used to individualize those buildings.…”

Section: Buildings From Lidar Datamentioning

confidence: 99%

DESCRIBING THE VERTICAL STRUCTURE OF INFORMAL SETTLEMENTS ON THE BASIS OF LIDAR DATA – A CASE STUDY FOR FAVELAS (SLUMS) IN SAO PAULO CITY

Ribeiro

Jarząbek-Rychard

Cintra

et al. 2019

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

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Abstract. Cadastral mapping of favela’s agglomerated buildings in informal settlements at Level of Detail 1 (LoD1) usually requires specific surveys and extensive manual data processing. Therefore, there is a demand for including the favelas in the city map production on the basis of Lidar surveys, as well as the detection of their vertical growth. However, the currently developed algorithms for automatically extracting buildings from airborne Lidar data have mainly been tested only for regular building reconstruction. This study aims to develop a Lidar data processing pipeline enabling to compute metrics related to intraurban informal settlements. To do so, we present a procedure to generate favela’s buildings delineation, height, floors’ number and built area and apply them to six case studies in favela typo-morphologies. We conducted an exploratory analysis in order to obtain the adequate parameters of the processing pipeline and its evaluation, using open source, free license and self-developed software. The results are compared to reference data from the manual stereo plotting, achieving a quality index in the building reconstruction about 70%. We also calculated the growth density, measured by gross Floor Area Ratio index inside settlement, revealing values from 29% to 74% considering different time periods.

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“…During previous decades, airborne light detection and ranging (LiDAR) technology, which is an efficient and rapid remote sensing technology for collecting three-dimensional (3D) point clouds over a large area, has been widely employed in various fields, such as digital terrain model (DTM) generation [1][2][3][4][5], forest ecosystem investigation [6][7][8][9][10][11][12], and 3D building modeling [13][14][15][16]. In most applications, a filtering operation for separating point clouds into ground and non-ground points is a preliminary and essential step.…”

Section: Introductionmentioning

confidence: 99%

Filtering Airborne LiDAR Data Through Complementary Cloth Simulation and Progressive TIN Densification Filters

et al. 2019

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Separating point clouds into ground and non-ground points is a preliminary and essential step in various applications of airborne light detection and ranging (LiDAR) data, and many filtering algorithms have been proposed to automatically filter ground points. Among them, the progressive triangulated irregular network (TIN) densification filtering (PTDF) algorithm is widely employed due to its robustness and effectiveness. However, the performance of this algorithm usually depends on the detailed initial terrain and the cautious tuning of parameters to cope with various terrains. Consequently, many approaches have been proposed to provide as much detailed initial terrain as possible. However, most of them require many user-defined parameters. Moreover, these parameters are difficult to determine for users. Recently, the cloth simulation filtering (CSF) algorithm has gradually drawn attention because its parameters are few and easy-to-set. CSF can obtain a fine initial terrain, which simultaneously provides a good foundation for parameter threshold estimation of progressive TIN densification (PTD). However, it easily causes misclassification when further refining the initial terrain. To achieve the complementary advantages of CSF and PTDF, a novel filtering algorithm that combines cloth simulation (CS) and PTD is proposed in this study. In the proposed algorithm, a high-quality initial provisional digital terrain model (DTM) is obtained by CS, and the parameter thresholds of PTD are estimated from the initial provisional DTM based on statistical analysis theory. Finally, PTD with adaptive parameter thresholds is used to refine the initial provisional DTM. These contributions of the implementation details achieve accuracy enhancement and resilience to parameter tuning. The experimental results indicate that the proposed algorithm improves performance over their direct predecessors. Furthermore, compared with the publicized improved PTDF algorithms, our algorithm is not only superior in accuracy but also practicality. The fact that the proposed algorithm is of high accuracy and easy-to-use is desirable for users.

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LiDAR Point Clouds to 3-D Urban Models$:$ A Review

Cited by 212 publications

References 162 publications

Automatic Recognition of Common Structural Elements from Point Clouds for Automated Progress Monitoring and Dimensional Quality Control in Reinforced Concrete Construction

Automatic Recognition of Common Structural Elements from Point Clouds for Automated Progress Monitoring and Dimensional Quality Control in Reinforced Concrete Construction

DESCRIBING THE VERTICAL STRUCTURE OF INFORMAL SETTLEMENTS ON THE BASIS OF LIDAR DATA – A CASE STUDY FOR FAVELAS (SLUMS) IN SAO PAULO CITY

Filtering Airborne LiDAR Data Through Complementary Cloth Simulation and Progressive TIN Densification Filters

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