A Calculating Method for the Height of Multi-Type Buildings Based on 3D Point Cloud

In sustainable urban development, Urban Building Energy Modeling (UBEM) and smart city initiatives depend on computable three-dimensional (3D) urban models. To enhance computational efficiency, research often uses Level of Detail (LOD) 1 simplified models for simulations. While LOD1 models simplify building geometry, obtaining accurate building height data typically requires substantial manual effort. Balancing model simplification with simulation accuracy is crucial for reliable and efficient results. This study proposes an automated method using open-source electronic maps and UAV oblique imagery to address 3D modeling challenges caused by incomplete databases. We developed computable 3D urban models at various LODs, with the highest detail achieved at LOD2.1. EnergyPlus was used to examine the relationship between LOD and energy use. The study follows five key steps: (1) Data Collection and Preprocessing—gather building footprints and UAV point clouds, refine point clouds to create voxels; (2) Roof Point Acquisition—extract and cluster roof points to determine building height; (3) Roof Type Identification—use machine learning to predict roof types; (4) Parametric Modeling and Simulation—construct databases and models at varying LODs for UBEM; (5) Results Comparison—evaluate modeling time, modeling accuracy, and energy use across LODs. This method demonstrates high accuracy, achieving a roof face identification accuracy of 92%, with building height errors within 1 meter and an average modeling time of just 2.5 seconds per building. Additionally, the roof type prediction model based on point cloud features attained an impressive AUC of 0.93. Increasing LOD slightly extended the energy simulation time, with LOD2.1 increasing simulation time by 6.5 seconds per building compared to LOD1_H_mean. The baseline model and LOD1_H_mean’s Energy Use Intensity (EUI) exhibited a MAPE of 3.02% and an RMSE of 4.41 kWh/m², underscoring the importance of high-LOD models for accurate simulations. It was found that LOD has a greater impact on heating than cooling, and building height errors significantly affect cooling simulations. These findings highlight the importance of accurate building height in hot regions for effective cooling and high-LOD modeling in cold regions, with assuming flat roofs where the type is uncertain helping to minimize errors. In summary, this study presents methods for automating the construction of high-LOD models for UBEM, also applicable to microclimate simulation and CIM. It demonstrates the significant impact of high-LOD on simulation accuracy and provides solutions for various simulation needs and data limitations, advancing UBEM and smart city development.

In recent years, due to the significant advancements in hardware sensors and software technologies, 3D environmental point cloud modeling has gradually been applied in the automation industry, autonomous vehicles, and construction engineering. With the high-precision measurements of 3D LiDAR, its point clouds can clearly reflect the geometric structure and features of the environment, thus enabling the creation of high-density 3D environmental point cloud models. However, due to the enormous quantity of high-density 3D point clouds, storing and processing these 3D data requires a considerable amount of memory and computing time. In light of this, this paper proposes a real-time 3D point cloud environmental contour modeling technique. The study uses the point cloud distribution from the 3D LiDAR body frame point cloud to establish structured edge features, thereby creating a 3D environmental contour point cloud map. Additionally, unstable objects such as vehicles will appear during the mapping process; these specific objects will be regarded as not part of the stable environmental model in this study. To address this issue, the study will further remove these objects from the 3D point cloud through image recognition and LiDAR heterogeneous matching, resulting in a higher quality 3D environmental contour point cloud map. This 3D environmental contour point cloud not only retains the recognizability of the environmental structure but also solves the problems of massive data storage and processing. Moreover, the method proposed in this study can achieve real-time realization without requiring the 3D point cloud to be organized in a structured order, making it applicable to unorganized 3D point cloud LiDAR sensors. Finally, the feasibility of the proposed method in practical applications is also verified through actual experimental data.

Accurate building height information is used for numerous urban applications, including infrastructure modeling, disaster risk assessment, and energy demand estimation. The U.S. Geological Survey collects airborne lidar data to support the three-dimensional elevation program (3DEP). The high point density and vertical accuracy of the lidar point clouds are optimal for 3D mapping. Few studies have systematically evaluated building height extraction from 3DEP lidar point clouds in urban areas. This study assesses the accuracy and consistency of building height estimates from two publicly available data sets by comparing them to the 3DEP lidar data. The public building height data sets are USA Structures and Microsoft Global Building Footprints (GBF), and they are evaluated across two contrasting urban sites. Building heights from USA Structures are also lidar-derived, whereas GBF estimates are based on stereo matching of optical imagery. Building heights from 3DEP were extracted by classifying lidar points using a deep learning model, isolating roof and ground returns, and computing the height as the median elevation difference between the roof and surrounding ground points. For both sites, 3DEP lidar-derived heights show strong agreement with USA Structures ( R ² = 0.87 and 0.86; MAE = 1.8 and 2.2 m), confirming the internal consistency and reliability of lidar-derived measurements. In contrast, comparisons with GBF height estimates reveal significantly lower correlations ( R ² = 0.54 and 0.60; MAE = 2.5 and 3.1 m), highlighting the limitations of imagery-based height reconstruction, particularly in dense or heterogeneous urban environments. These findings indicate that lidar can be used as an accurate source for vertical structure characterization and underscore the benefits of improved integration and bias correction when using imagery-derived building heights. The results provide support for hybrid approaches that combine the broad spatial coverage of optical data sets with the accuracy of lidar for scalable 3D urban mapping.

Converting three-dimensional (3D) point cloud data to two-dimensional (2D) image based on virtual structured-light system is a popular method in 3D data compression because the image format is easier to process by the exiting storing and transmitting methods. When this method is used in the 3D point cloud compression, the quantization error is introduced during the coordinate mapping. To solve this problem, a virtual structured-light 3D point cloud compression algorithm based on geometric reshaping is presented. And a least-square system parameter optimization method is proposed to further improve the data decoding accuracy. In the proposed method, the 3D spatial coordinates are reshaped to a 2D matrix first, and then the 2D matrix is stored as a “Holoimage” by using the parameter optimized virtual structured-light system. The quantization error introduced by coordinate mapping between X and Y coordinates of the 3D point cloud and the 2D image pixel coordinates is suppressed, so the decoding accuracy is improved. In addition, the geometric information of the 3D point cloud is hidden synchronously when the 3D point cloud is compressed, which is of great significance in the copyright protection of the point cloud data. Experiments verify the effectiveness of the proposed algorithm. The decoding root mean square error (RMSE) of the proposed method is decreased by 79.86% on average compared with the traditional one under the same compression ratio, and the compression ratio of the proposed method is 1.96 times bigger than the traditional one under the similar decoding accuracy.

Along with increasingly popular virtual reality applications, the three-dimensional (3D) point cloud has become a fundamental data structure to characterize 3D objects and surroundings. To process 3D point clouds efficiently, a suitable model for the underlying structure and outlier noises is always critical. In this work, we propose a hypergraph-based new point cloud model that is amenable to efficient analysis and processing. We introduce tensor-based methods to estimate hypergraph spectrum components and frequency coefficients of point clouds in both ideal and noisy settings. We establish an analytical connection between hypergraph frequencies and structural features. We further evaluate the efficacy of hypergraph spectrum estimation in two common applications of sampling and denoising of point clouds for which we provide specific hypergraph filter design and spectral properties. Experimental results demonstrate the strength of hypergraph signal processing as a tool in characterizing the underlying properties of 3D point clouds.

Estimation of plant parameters is prerequisite for fast and precise management of tomato crops on commercial farm level. Destructive estimation methods may be accurate but not suitable when large farm size, spatio-temporal variability or short period of time to obtain information are considered. Therefore, this study was aimed to estimate leaf area (LA) in mature tomato plants from reconstructed geometric plant model based on 3-dimensional (3D) point cloud. A 2D laser scanner was placed on a programable linear conveyor system to move at controlled forward speed. To achieve a high density 3D point cloud of whole plant, close range (< 2 m) scanning was performed at 10 mm/s forward speed. Plant parameters capturing points per plant (PPP), plant height, plant volume, and LA were extracted from the 3D point clouds. Destructive method was utilized in laboratory for reference LA data collection, whereas other required reference data (plant height and canopy area) were measured in-situ. Linear regression models were developed and cross validated to estimate LA using variables from the 3D point clouds and reference LA. Among the models, plant height showed strong correlation with reference LA (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.90, RMSE = 40 cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) and followed by PPP (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.89, RMSE = 45 cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), voxel grid approach (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.56, RMSE = 58 cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) and slicing approach (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.44, RMSE = 70 cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ). Results indicate that mobile laser scanning could be a reliable non-destructive LA estimation tool for tomato and may be adapted by the commercial tomato growers for profitable management decisions.

In recent years, there has been significant growth in the ubiquity and popularity of three-dimensional (3D) point clouds, with an increasing focus on the classification of 3D point clouds. To extract richer features from point clouds, many researchers have turned their attention to various point set regions and channels within irregular point clouds. However, this approach has limited capability in attending to crucial regions of interest in 3D point clouds and may overlook valuable information from neighboring features during feature aggregation. Therefore, this paper proposes a novel 3D point cloud classification method based on global attention and adaptive graph convolution (Att-AdaptNet). The method consists of two main branches: the first branch computes attention masks for each point, while the second branch employs adaptive graph convolution to extract global features from the point set. It dynamically learns features based on point interactions, generating adaptive kernels to effectively and precisely capture diverse relationships among points from different semantic parts. Experimental results demonstrate that the proposed model achieves 93.8% in overall accuracy and 90.8% in average accuracy on the ModeNet40 dataset.

The present work proposes a new method Reg3DFacePtCd for registration of point clouds. The key contribution of the present method is that an unknown face in 3D point cloud form is given to the system and is registered to the already existing known 3D face point clouds using a fast 3D face registration method. The novelty of the present technique is that at first the alignment and registration parameters are found out by initially registering eight key points of the unknown source model to that of the known model. Next, the rest of the point clouds of the unknown model are registered to that of the known model using the same parameters found as above. The main method used for alignment is iterative closest point (ICP) using point-based technique followed by registration in the least squares sense. Mainly there are two significant contributions. Firstly, we have developed a new mathematical model facial landmark point based model across poses to obtain the target or the known model to which all the unknown models will be registered. Secondly, a novel way to accelerate point cloud matching by reducing the number of points has been proposed. Using a small number of points necessarily would speed up the registration process but may inculcate errors. So, to determine the registration quality of the fundamental eight key points on which the entire registration process is based, a new robust metric namely ICV (ICP certainty vector) consisting of several key components have been used. Finally, we have addressed several important face registration issues like pre-processing, convergence and quality of registration of the entire facial point cloud based on the eight key points. Extensive experimentation on Frav3D, GavabDB, and the Bosphorus databases on a high-performance computing environment show the novelty and robustness of the method.

Automated recognition and rebar dimensional assessment of prefabricated bridge components from low-cost 3D laser scanner

The inclusion of thermal infrared (TIR) data in point clouds derived from unmanned aircraft system (UAS) imagery can benefit a variety of applications in which surface temperature and 3D geometry are both important discriminants of feature type and condition. Low resolution and narrow fields of view (FOV) of current consumer-grade TIR cameras on UAS, combined with the lack of sharpness and texture in many image regions, may cause failure or poor results from structure from motion (SfM) photogrammetric software, which has gained widespread use for generating point clouds from UAS imagery. This paper proposes a photogrammetric approach for generating 3D multispectral point clouds utilizing coacquired TIR-RGB images. A 3D point cloud is first generated from the RGB imagery using standard SfM procedures. Then the TIR attributes are assigned to points, where the image coordinates of the points in TIR images are estimated using transformation parameters obtained from co-registration procedures. To obtain RGB-to-TIR transformation parameters, this study tests 3D and 2D co-registration approaches. The latter produces better results due to the challenge of calibrating the TIR camera as required for the 3D approach. This proposed approach is advantageous for generating TIR point clouds without loss of photogrammetric precision compared with solely TIR-based SfM, as the 3D accuracy, point density, and reliability are greatly enhanced.

Surface defect identification is a crucial task in many manufacturing systems, including automotive, aircraft, steel rolling, and precast concrete. Although image-based surface defect identification methods have been proposed, these methods usually have two limitations: images may lose partial information, such as depths of surface defects, and their precision is vulnerable to many factors, such as the inspection angle, light, color, noise, etc. Given that a three-dimensional (3D) point cloud can precisely represent the multidimensional structure of surface defects, we aim to detect and classify surface defects using a 3D point cloud. This has two major challenges: (i) the defects are often sparsely distributed over the surface, which makes their features prone to be hidden by the normal surface and (ii) different permutations and transformations of 3D point cloud may represent the same surface, so the proposed model needs to be permutation and transformation invariant. In this paper, a two-step surface defect identification approach is developed to investigate the defects’ patterns in 3D point cloud data. The proposed approach consists of an unsupervised method for defect detection and a multi-view deep learning model for defect classification, which can keep track of the features from both defective and non-defective regions. We prove that the proposed approach is invariant to different permutations and transformations. Two case studies are conducted for defect identification on the surfaces of synthetic aircraft fuselage and the real precast concrete specimen, respectively. The results show that our approach receives the best defect detection and classification accuracy compared with other benchmark methods.

In recent years, machine vision has played an important role in product surface quality detection. The promotion and use of this technology have largely avoided the subjectivity caused by human detection and improved detection efficiency and accuracy. Different from the image data commonly used in machine vision, point cloud can describe the spatial structure of an object, provide more information than image data, and have the ability to expand the data to build multi-dimensional data models. Due to the strong anti-interference ability of point cloud sensors and the high accuracy of three-dimensional positioning information point cloud, nondestructive testing technology based on point cloud has received more and more attention. This paper summarizes the research progress of product surface quality detection methods based on 3D point cloud in recent years. According to different data processing methods, the detection research is divided into five categories: based on point cloud contour, based on local geometric feature, based on template matching, based on multimodal point cloud, and based on deep learning. The five methods are reviewed and summarized respectively. Finally, the key problems of each detection method and the future trend of product surface quality detection technology based on 3D point cloud are discussed.

The Ming and Qing Dynasty type of official-style architecture roof can provide plenty of prior knowledge relating to the structure and size of these works of architecture, and plays an important role in the fields of 3D modeling, semantic recognition and culture inheriting. In this paper, we take the 3D point cloud as the data source, and an automatic classification method for the roof type of Ming and Qing Dynasty official-style architecture based on the hierarchical semantic network is illustrated. To classify the roofs into the correct categories, the characteristics of different roof types are analyzed and features including SoRs, DfFtR, DoPP and NoREs are first selected; subsequently, the corresponding feature extraction methods are proposed; thirdly, aiming at the structure of the ridges, a matching graph relying on the attributed relational graph of the ridges is given; based on the former work, a hierarchical semantic network is proposed and the thresholds are determined with the help of the construction rules of the Ming and Qing Dynasty official-style architecture. In order to fully verify the efficiency of our proposed method, various types of Ming and Qing Dynasty official-style architecture roof are identified, and the experimental results show that all structures are classified correctly.

Unmixing urban hyperspectral imagery using probability distributions to represent endmember variability

Rockfall event is common on rocky terrain, and it threatens people. Recent advances in remote sensing technologies allow us to model the terrain with high accuracy and resolution. In this study, we show the application of modeling a rockfall site in three-dimensional (3D) using an unmanned aerial vehicle. The acquisition of information about discontinuities in the rock masses using 3D point clouds is vital to characterize rockfall dynamics. We obtained 3D spatial information and point clouds of a rocky slope from acquired images. We extracted 10 discontinuity sets from the 3D model as they have significant effects on the failure mechanism. Details in mapping discontinuities have improved significantly with the help of point clouds. The results are beneficial for modeling the terrain of inaccessible areas with high resolution and obtaining information from those areas. This paper shows how can point cloud analysis be applied to characterize discontinuity sets.