3D Building Models Research Articles

Array Three-Dimensional SAR Imaging via Composite Low-Rank and Sparse Prior

Array three-dimensional (3D) synthetic aperture radar (SAR) imaging has been used for 3D modeling of urban buildings and diagnosis of target scattering characteristics, and represents one of the significant directions in SAR development in recent years. However, sparse driven 3D imaging methods usually only capture the sparse features of the imaging scene, which can result in the loss of the structural information of the target and cause bias effects, affecting the imaging quality. To address this issue, we propose a novel array 3D SAR imaging method based on composite sparse and low-rank prior (SLRP), which can achieve high-quality imaging even with limited observation data. Firstly, an imaging optimization model based on composite SLRP is established, which captures both sparse and low-rank features simultaneously by combining non-convex regularization functions and improved nuclear norm (INN), reducing bias effects during the imaging process and improving imaging accuracy. Then, the framework that integrates variable splitting and alternative minimization (VSAM) is presented to solve the imaging optimization problem, which is suitable for high-dimensional imaging scenes. Finally, the performance of the method is validated through extensive simulation and real data experiments. The results indicate that the proposed method can significantly improve imaging quality with limited observational data.

Integration Design of Chinese Painting and Architectural Space Based on Virtual Reality Technology

Chinese painting elements are important elements in modern architectural design, which facilitate the integration of the entire building into the environment and present the scroll style spatial hierarchy of Chinese painting. Therefore, it is necessary to research on the integration design method of Chinese painting and architectural space based on virtual reality technology. First, the architectural space and landscape design plan images integrated with Chinese painting are imported into VTK software. Second, the median filtering method is used to filter the imported images, and a secure multi-party calculation method is used to construct building and landscape models, thus generating a database of architectural spatial layout element models. Then, for the 3D models in the database, Chinese painting elements are applied to architectural space design. Model rendering is achieved through lighting, color, atomization, and other processing to enhance the visual appeal of the models. Next, using the rendered 3D models of buildings and landscapes, a virtual interactive architectural spatial environment background is achieved through the freehand artistic conception concept and framing techniques in Chinese painting. Finally, we design a multi-sensory visual interactive interface based on virtual reality to display the results of the architectural spatial environment scenery to users. The experimental results show that this method has high modeling and rendering accuracy, and can achieve landscape layout browsing from different angles, improving user experience.

Geological objects and structures in 3D: observation, interpretation and building of 3D Models

Geological objects and structures in 3D: observation, interpretation and building of 3D Models, by D. Frizon de Lamotte, P. Leturmy, P. Souloumiac & A. Frizon de Lamotte, 2021. CRC Press, London, 138 pages. Paperback: price GBP 39.09, ISBN 978-0367497507

Study on the genesis of deposits of the Productive Series of the Bibi-Heybat field by using a complex of borehole data

The majority of developed oil and gas fields in our country are associated with deposits of terrigenous origin. The results of the research carried out provide a basis to conclude that terrigenous reservoirs are not completely homogeneous. Generally, the individual layers differ in their composi’tion in terms of porosity, permeability, shape, size, etc. Therefore, the heterogeneous characteristics of reservoirs should be determined in advance. For this reason, the study of sedimentation conditions of terrigenous layers and the genesis of deposits can be useful for geologists and geophysicists when discovering new fields. This article examines the issue of studying the genesis of deposits of the Productive Series based on the results of quantitative interpretation of borehole data. Sections of wells NoNo X1, X2, X3, X4 from horizons X, XI, XII, XIII, XIV, and XV of the Productive Series (PS) were conditionally chosen as the area of research in the Bibi-Heybat field. The values of effective porosity and volumetric clay content were calculated using established methods and formulas for selected well sections, and histograms of the variation of these values with depth were plotted. The genesis of deposits was studied on the basis of histograms, and facies of stream, bar, and beach-plain origin were identified. The article also defines intervals for the identification of stream, bar, and beach-plain facies in the studied areas and presents 2D and 3D models characterizing the distribution of their thicknesses in the studied region. The research work also involved building 3D models of the distribution of determined facies thicknesses as well as effective porosity, volumetric clay content, and oil and gas saturation across the wells. According to the 3D model analyses, the permeability coefficient value for the study area ranges from 0-120 mD, the porosity value ranges from 0-20%, and the oil and gas saturation ratio value ranges from 0-100%.

Comparative Analysis of Energy Efficiency in Conventional, Modular, and 3D-Printing Construction Using Building Information Modeling and Multi-Criteria Decision-Making

Energy efficiency has become a crucial focus with the growing attention on sustainable development and decreasing energy consumption in the built environment. Different construction methods are being applied worldwide, such as conventional, modular, and 3D-printing methods, to increase energy efficiency in buildings. This study aims to enhance the decision-making process by identifying optimal construction techniques, material selection, and ventilation window dimensions to promote sustainable energy use in buildings. A novel framework combining Building Information Modeling (BIM), computational analysis, and Multi-Criteria Decision-Making (MCDM) approaches is applied to assess the energy use intensity (EUI), annual electric energy consumption, and lifecycle energy cost across multiple sequences for each type of construction. Computational analysis in this research is combined in two main tools. Minitab is utilized for experimental design to determine the number and configurations of sequences analyzed. The Simple Additive Weighting (SAW) method, applied as an MCDM tool, is used to assess and rank the performance of sequences based on equally weighted criteria. Subsequently, 3D models of case study buildings are developed, and energy simulations are conducted using Autodesk Revit and Autodesk Green Building Studio, respectively, as BIM tools to compare the energy performance of various design alternatives. The results revealed that 3D printing surpassed other methods, where Sequence 7 achieved approximately 10.3% higher efficiency than modular methods and 40.5% better performance than conventional methods in the evaluated criteria. The findings underscore the higher energy efficiency of 3D printing, followed by modular construction as a competitive method, while conventional methods lagged significantly.

Automatic upgrade of 3D building models to LoD3 using 3D Point Clouds and Grounding DINO

Abstract. The advancement of urban digital twins depends on the accurate representation of 3D city models, particularly Level of Detail 3 (LoD3) models, which incorporate detailed façade features essential for urban planning applications. However, generating LoD3 models is challenging due to the complexities of semantic segmentation in 3D point cloud data and the high resource demands of traditional methods. This paper presents an automated methodology for upgrading existing Level of Detail 2.2 (LoD2.2) building models to LoD3 using mobile mapping point cloud data and the Grounding DINO model. The approach begins with extracting façade surfaces from LoD2.2 models while maintaining geometric integrity. Point cloud data is then transformed into a 2D image format to facilitate the application of Grounding DINO, which accurately detects and segments façade elements such as windows and doors. The identified features are re-integrated into the 3D model, resulting in an enhanced LoD3 representation. This methodology demonstrates effectiveness and scalability, providing a practical solution for improving urban digital twins with detailed and reliable building models.

Vertex-Oriented Method for Polyhedral Reconstruction of 3D Buildings Using OpenStreetMap.

This work presents the mathematical definition and programming considerations of an efficient geometric algorithm used to add roofs to polyhedral 3D building models obtained from OpenStreetMap. The algorithm covers numerous roof shapes, including some well-defined shapes that lack an explicit reconstruction theory. These shapes include gabled, hipped, pyramidal, skillion, half-hipped, gambrel, and mansard. The input data for the developed code consist of latitude and longitude coordinates defining the target area. Geospatial data necessary for the algorithm are obtained through a request to the overpass-turbo service. The findings showcase outstanding performance for buildings with straightforward footprints, but they have limitations for the ones with intricate footprints. In future work, further refinement is necessary to solve the mentioned limitation.

PELATIHAN BUILDING INFORMATION MODELLING BERBASIS TEKLA STRUCTURES PADA SISWA SEKOLAH MENENGAH KEJURUAN KELAS XI DI KABUPATEN MOJOKERTO

Vocational High School (SMK) graduates contributed the most to the percentage of TPT (Open Unemployment Rate) as of August 2022 of 5.86% or 8.42 million people. The importance of increasing the involvement of partners in the world of work in designing and managing learning in vocational schools and developing the potential of students' abilities and expertise that are in line with opportunities to work in the world of work and/or entrepreneurship can reduce the TPT score. The results of direct observations at schools and confirmed based on the provisions of BSKAP 033/H/KR/2022, the learning achievements of students have not achieved their competencies in either phase E or F, namely regarding the ability to model 3D pictorial projections using BIM technology. The purpose of this PKM in the field of BIM applications is to improve students' skills in applying Building Information Modeling in achieving 3D modeling abilities and competencies. The empowerment method applied is in the form of training and mentoring of BIM applications in 3D modeling of construction buildings to improve the skills of partners related to drawing 3D building structures with BIM technology integrated with software. From the results of the initial and final assessment analysis, it was found that there was an increase in partner skills related to drawing 3D building structures with BIM technology integrated with software of ≥ 70% based on the performance of the 3D modeling simulation that was built after participating in the training as well as an increase in skills in creating informative construction design visualizations with software of ≥ 70%.

A real scene 3D Model-Driven sunlight analysis method for complex building roofs

A real-scene 3D model of complex buildings, derived from UAV (Unmanned Aerial Vehicle) surveys, can significantly improve the accuracy of sunlight analysis for the arrangement of photovoltaic panels. We propose a method for sunlight analysis of complex building roofs driven by the real-scene 3D model, which includes generating and optimizing the 3D model and a parameterized sunlight analysis algorithm. The generation and optimization method involves: reducing the number of model meshes by selecting a lower level of detail and proposing a mesh simplification algorithm to simplify the model; reconstructing the structure of the model meshes to smooth them and solve the pseudo-occlusion problems caused by the model’s triangular structures by transforming triangular meshes into quadrilateral meshes; improving the accuracy of the obstacles’ 3D models on the roof by completing high-precision obstacle modeling and superimposing it on the simplified model. Subsequently, a parameterized sunlight analysis algorithm suited to the optimized 3D model is presented based on the Grasshopper parameterized software platform. We design a complete set of sunlight analysis algorithm programs by exploring the geographical location, time range, time step, and other parameters of the real-scene 3D model. Finally, the method’s feasibility is verified through a case study of a complex building.

Fast Building Instance Proxy Reconstruction for Large Urban Scenes.

Digitalization of large-scale urban scenes (in particular buildings) has been a long-standing open problem, which attributes to the challenges in data acquisition, such as incomplete scene coverage, lack of semantics, low efficiency, and low reliability in path planning. In this paper, we address these challenges in urban building reconstruction from aerial images, and we propose an effective workflow and a few novel algorithms for efficient 3D building instance proxy reconstruction for large urban scenes. Specifically, we propose a novel learning-based approach to instance segmentation of urban buildings from aerial images followed by a voting-based algorithm to fuse the multi-view instance information to a sparse point cloud (reconstructed using a standard Structure from Motion pipeline). Our method enables effective instance segmentation of the building instances from the point cloud. We also introduce a layer-based surface reconstruction method dedicated to the 3D reconstruction of building proxies from extremely sparse point clouds. Extensive experiments on both synthetic and real-world aerial images of large urban scenes have demonstrated the effectiveness of our approach. The generated scene proxy models can already provide a promising 3D surface representation of the buildings in large urban scenes, and when applied to aerial path planning, the instance-enhanced building proxy models can significantly improve data completeness and accuracy, yielding highly detailed 3D building models.

Estimation of Longwave Radiation Intensity Emitted from Urban Obstacles in Each Direction Using Drone-Based Photogrammetry

Longwave radiation is a crucial factor affecting human thermal comfort and thermal stress, especially in outdoor spaces in summer, owing to the vast effect of longwave radiation emitted from high-heated asphalt roads, building walls, and automobiles. Although controlling the longwave radiation environment to improve thermal comfort in summer is crucial, the prediction of the longwave radiation environment is frequently conducted only at the assessment stage of the final proposal because of the high computational cost of radiation calculations and unsteady heat balance analysis considering multiple reflections. This is a significant constraint for the design of urban and architectural environments. A previous study proposed a method to rapidly estimate the longwave radiation environment based on a point-by-point method with longwave radiation intensity distributions of the heat sources. To use this method, 3D models of the geographical objects in urban areas, such as buildings and trees, must be accurately generated, and these models should have information on the longwave radiation emitted in each direction from each object. However, no specific examples of a 3D model and longwave radiation intensity distribution have been presented. In this study, a 3D modeling method for geographical objects in urban areas with longwave radiation information based on drones and photogrammetric techniques was utilized. Moreover, a 3D model of a small-scale building was generated. A longwave radiation intensity distribution was produced for the building. Based on the distribution data, the directional characteristics of longwave radiation were discussed, and the availability of the proposed method was assessed.

Open Data-Driven 3D Building Models for Micro-Population Mapping in a Data-Limited Setting

Urban planning and management increasingly depend on accurate building and population data. However, many regions lack sufficient resources to acquire and maintain these data, creating challenges in data availability. Our methodology integrates multiple data sources, including aerial imagery, Points of Interest (POIs), and digital elevation models, employing Light Gradient Boosting Machine (LightGBM) and Gradient Boosting Decision Tree (GBDT) to classify building uses and morphological filtration to estimate heights. This research contributes to bridging the gap between data needs and availability in resource-constrained urban environments, offering a scalable solution for global application in urban planning and population mapping.

High-precision 3D Modeling of Construction Waste Pile Bodies by Integrating Multi-source Data

Abstract. The instability of construction waste pile bodies, as an increasingly concerning disaster, poses significant risks to the safety of people's lives and property. Currently, there is limited research on high-precision 3D modeling techniques for construction waste pile bodies, which significantly hinders the accuracy and reliability of early detection and risk assessment of pile body instability. Therefore, constructing high-precision 3D models of construction waste pile bodies using multi-source data plays a crucial role in improving the accuracy and timeliness of early warning systems for pile body instability, offering significant theoretical research and practical application value. Building 3D models of construction waste pile bodies solely based on unmanned aerial vehicle (UAV) oblique photography data faces challenges, such as various degrees of data voids and insufficient model completeness, and few methods currently address the construction of 3D models of construction waste pile bodies through the fusion of multi-source data. This paper attempts to use the Iterative Closest Point (ICP) algorithm, combining SLAM laser point cloud data with UAV oblique photography data to fill data voids, and utilizes the fused point cloud to reconstruct the triangulation network, achieving the transformation from 3D point cloud models to 3D surface models. On the basis of texture mapping, a high-precision 3D model of the construction waste pile body is constructed. The research results show that the 3D model of the construction waste pile body, integrated with multi-source data, has a planar error of 0.0187m and an elevation error of 0.0368m, meeting the corresponding model accuracy requirements. It can more realistically restore the fine 3D features of the construction waste pile body, effectively compensating for the shortcomings of single-source data in 3D modeling of construction waste pile bodies, providing a new method for the 3D model reconstruction of construction waste pile bodies, and offering effective data support for construction waste research.

An Algorithm for Simplifying 3D Building Models with Consideration for Detailed Features and Topological Structure

To tackle problems such as the destruction of topological structures and the loss of detailed features in the simplification of 3D building models, we propose a 3D building model simplification algorithm that considers detailed features and topological structures. Based on the edge collapse algorithm, the method defines the region formed by the first-order neighboring triangles of the endpoints of the edge to be collapsed as the simplification unit. It incorporates the centroid displacement of the simplification unit, significance level, and approximate curvature of the edge as influencing factors for the collapse cost to control the edge collapse sequence and preserve model details. Additionally, considering the unique properties of 3D building models, boundary edge detection and face overlay are added as constraints to maintain the model’s topological structure. The experimental results show that the algorithm is superior to the classic QEM algorithm in terms of preserving the topological structure and detailed features of the model. Compared to the QEM algorithm and the other two comparison algorithms selected in this paper, the simplified model resulting from this algorithm exhibit a reduction in Hausdorff distance, mean error, and mean square error to varying degrees. Moreover, the advantages of this algorithm become more pronounced as the simplification rate increases. The research findings can be applied to the simplification of 3D building models.

A boundary-aware point clustering approach in Euclidean and embedding spaces for roof plane segmentation

Roof plane segmentation from airborne light detection and ranging (LiDAR) point clouds is an important technology for three-dimensional (3D) building model reconstruction. One of the key issues of plane segmentation is how to design powerful features that can exactly distinguish adjacent planar patches. The quality of point feature directly determines the accuracy of roof plane segmentation. Most of existing approaches use handcrafted features, such as point-to-plane distance, normal vector, etc., to extract roof planes. However, the abilities of these features are relatively low, especially in boundary areas. To solve this problem, we propose a boundary-aware point clustering approach in Euclidean and embedding spaces constructed by a multi-task deep network for roof plane segmentation. We design a three-branch multi-task network to predict semantic labels, point offsets and extract deep embedding features. In the first branch, we classify the input data as non-roof, boundary and plane points. In the second branch, we predict point offsets for shifting each point towards its respective instance center. In the third branch, we constrain that points of the same plane instance should have the similar embeddings. We aim to ensure that points of the same plane instance are close as much as possible in both Euclidean and embedding spaces. However, although deep network has strong feature representative ability, it is still hard to accurately distinguish points near the plane instance boundary. Therefore, we first robustly group plane points into many clusters in Euclidean and embedding spaces to find candidate planes. Then, we assign the rest boundary points to their closest clusters to generate the final complete roof planes. In this way, we can effectively reduce the influence of unreliable boundary points. In addition, to train the network and evaluate the performance of our approach, we prepare a synthetic dataset and two real datasets. The experiments conducted on synthetic and real datasets show that the proposed approach significantly outperforms the existing state-of-the-art approaches in both qualitative evaluation and quantitative metrics. To facilitate future research, we will make datasets and source code of our approach publicly available at https://github.com/Li-Li-Whu/DeepRoofPlane.

Multi-sensor points cloud data fusion for 3D building models: A case study in Halong City, Vietnam

Multi-sensor points cloud data fusion for 3D building models: A case study in Halong City, Vietnam

Simplification algorithm of 3D building model based on triangle folding

3D building models have a complex structure and a large number of triangular meshes, which can put great pressure on computer real-time rendering. Therefore, the model needs to be simplified to reduce the number of meshes without affecting the overall visual effect of the model. The algorithm in this paper is based on triangle folding, which simplifies more but loses model details. In order to make up for the shortcoming of triangle folding, more constraints need to be introduced for error control. Meanwhile, this paper solves the problem of higher complexity of the algorithm due to the introduction of more constraints by calculating the Mahalanobis distance of each constraint factor introduced. The vertices with obvious sharp features are constrained to keep the structure of the model undeformed. Experiments prove that the algorithm in this paper can better preserve the detailed features and the visual effect of the model.

Spatial Analysis with Detailed Indoor Building Models for Emergency Services

This paper presents a systematic approach to perform spatial analysis with detailed indoor building models for emergency service decision supports. To achieve a more realistic spatial application, this research integrates three-dimensional (3D) indoor building models and their attributes to simulate an emergency evacuation scenario. Indoor building models of a complicated train station with different levels of detail are generated from two-dimensional (2D) floor plans and Building Information Model (BIM) datasets. In addition to the 3D building models, spatial and non-spatial attributes are also associated with the created building models and the objects within them. The ant colony optimization (ACO) algorithm is modified to analyze the indoor building models for emergency service decision support applications. The detailed indoor models and the proposed spatial analysis algorithms are tested in simulated emergency evacuation scenarios to select the best routes during emergency services. The experimental results demonstrate that the proposed system is helpful for selecting the optimal route with the least cost at varying time stamps. Together with the developed spatial analysis framework, they have a great potential for effective decision support during emergency situations.

Automatic high-detailed building reconstruction workflow for urban microscale simulations

Reconstructing urban scenarios for computational fluid dynamics simulations typically requires significant manual effort, especially when higher geometrical details are required. To address this issue, we present a workflow to automatically reconstruct buildings in three levels of detail (LoDs): LoD1.2, LoD1.3, and LoD2.2, tailored to urban microscale simulations. The workflow uses a combination of building footprints and a point cloud to segment roof planes, create partitions, optimise planes, and finally assemble roof planes into 3D building models. Reconstructed buildings are seamlessly integrated into the terrain together with different surface layers such as water, low vegetation, and paved surfaces. Apart from three general LoDs, building footprints can be simplified as a part of the 2D generalisation; additionally, smaller surfaces such as chimneys and ventilation shafts can be removed using a graph-cut optimisation. The integrated geometry validator can report on validity of building models, such as watertightness, manifoldness, or occurrences of self-intersections. In the case of invalid geometries, we can generate an approximation: geometry repair the with alpha wrapping algorithm, or reconstruction in lower LoD. We tested our implementation on two different real-world datasets — one in The Netherlands, and another one in the USA. The results showed that 95% (Dutch dataset) and 90% (US dataset) buildings were valid according to the ISO 19107 standard. Generated grids showed satisfactory quality as we observed monotonous convergence in simulations with grid convergence indices up to 3.8% for pressure and velocity variables. These results indicate that the workflow is suitable for typical urban microscale simulations.

LCZ-based city-wide solar radiation potential analysis by coupling physical modeling, machine learning, and 3D buildings

Addressing climate change and urban energy problems is a great challenge. Building Integrated Photovoltaics (BIPV) plays a pivotal role in energy conservation and carbon emission reduction. However, traditional approaches to assessing solar radiation on buildings with physical models are computing-intensive and time-consuming. This study presents a hybrid approach by integrating physical model-based solar radiation calculation and machine learning (ML) for city-wide building solar radiation potential (SRP) analysis. By considering urban morphology, land cover, and meteorological characteristics, local climate zones (LCZs) are classified. The SRP of representative LCZs is precisely evaluated using computing-intensive physical models integrated with 3D building models. A ML model is then developed to effectively predict the SRP of building roofs and facades throughout the city. An experiment was conducted in Shenzhen, China to validate the presented approach. The results demonstrate that Shenzhen has a total annual building solar radiation of 3.28∗1011kwh. Luohu District exhibits the highest SRP density. The LCZ-based analysis highlights that compact low-rise LCZs offer greater SRP for roofs, while compact high-rise LCZs do so for facades. Moreover, BIPV could cut CO2 emission by up to 41.85 million tons annually. Notably, solar PV installation only on rooftops in Shenzhen could meet 87.81% of the city's electricity department's carbon reduction goal. This study provides an alternative for city-wide SRP estimation by combining physical modeling and ML and offers valuable insights for data-driven and model-driven urban planning and management in low-carbon cities.