Reconstruction Of 3D Building Models Research Articles

Water Conservancy Hub Geometric Modeling Based on Point Clouds

For hydraulic engineering, accurate geometric modeling is crucial for design, analysis, and maintenance. In recent years, point cloud technology, with its high precision and detailed data capture capabilities, has gradually become the preferred method for geometric modeling in hydraulic engineering. However, traditional point cloud processing methods exhibit slow data processing speeds and poor mesh quality when reconstructing 3D models. Therefore, we have studied the meshing method for the 3D reconstruction of large-scale discrete point cloud data and proposed a meshing method based on the Delaunay criterion and the growth method to achieve rapid meshing of discrete point cloud data. To demonstrate the efficiency and convenience of the proposed method, point cloud reconstruction experiments are conducted on various buildings. We compare our approach with the Poisson, BPA, and ONet methods. Additionally, we apply it to the Jiangsu Gaogang Water Conservancy Hub in China. The results show that our proposed method is superior in both reconstruction quality and efficiency, providing a promising solution for the reconstruction of 3D building models from point clouds.

Extraction of Building Roof Contours from Airborne LiDAR Point Clouds Based on Multidirectional Bands

Because of the complex structure and different shapes of building contours, the uneven density distribution of airborne LiDAR point clouds, and occlusion, existing building contour extraction algorithms are subject to such problems as poor robustness, difficulty with setting parameters, and low extraction efficiency. To solve these problems, a building contour extraction algorithm based on multidirectional bands was proposed in this study. Firstly, the point clouds were divided into bands with the same width in one direction, the points within each band were vertically projected on the central axis in the band, the two projection points with the farthest distance were determined, and their corresponding original points were regarded as the roof contour points; given that the contour points obtained based on single-direction bands were sparse and discontinuous, different banding directions were selected to repeat the above contour point marking process, and the contour points extracted from the different banding directions were integrated as the initial contour points. Then, the initial contour points were sorted and connected according to the principle of joining the nearest points in the forward direction, and the edges with lengths greater than a given threshold were recognized as long edges, which remained to be further densified. Finally, each long edge was densified by selecting the noninitial contour point closest to the midpoint of the long edge, and the densification process was repeated for the updated long edge. In the end, a building roof contour line with complete details and topological relationships was obtained. In this study, three point cloud datasets of representative building roofs were chosen for experiments. The results show that the proposed algorithm can extract high-quality outer contours from point clouds with various boundary structures, accompanied by strong robustness for point clouds differing in density and density change. Moreover, the proposed algorithm is characterized by easily setting parameters and high efficiency for extracting outer contours. Specific to the experimental data selected for this study, the PoLiS values in the outer contour extraction results were always smaller than 0.2 m, and the RAE values were smaller than 7%. Hence, the proposed algorithm can provide high-precision outer contour information on buildings for applications such as 3D building model reconstruction.

3D RECONSTRUCTION OF SIMPLE BUILDINGS FROM POINT CLOUDS USING NEURAL NETWORKS WITH CONTINUOUS CONVOLUTIONS (CONVPOINT)

Abstract. The automatic reconstruction of 3D building models from airborne laser scanning point clouds or aerial imagery data in a model-driven fashion most often consists of a recognition of standardized building primitives with typically rectangular footprints and parameterized roof shapes based on a pre-defined collection, and a parameter estimation so that the selected primitives best fit the input data. For more complex buildings that consist of multiple parts, several such primitives need to be combined. This paper focuses on the reconstruction of such simple buildings, and explores the potential of Deep Learning by presenting a neural network architecture that takes a 3D point cloud of a single building as input and outputs the geometric information needed to construct a 3D building model in half-space representation with up to four roof faces like saddleback, hip, and pyramid roof. The proposed neural network architecture consists of a roof face segmentation module implemented with continuous convolutions as used in ConvPoint, which performs feature extraction directly from a set of 3D points, and four PointNet modules that predict from sampled subsets of the feature-enriched points the presence of four roof faces and their slopes. Trained with the RoofN3D dataset, which contains roof point segmentations and geometric information for 3D reconstruction purposes for about 118,000 simple buildings, the neural network achieves a performance of about 80% intersection over union (IoU) for roof face segmentation, 1.8° mean absolute error (MAE) for roof slope angles, and 95% overall accuracy (OA) for predicting the presence of faces.

3D building reconstruction from single street view images using deep learning

3D building models are an established instance of geospatial information in the built environment, but their acquisition remains complex and topical. Approaches to reconstruct 3D building models often require existing building information (e.g. their footprints) and data such as point clouds, which are scarce and laborious to acquire, limiting their expansion. In parallel, street view imagery (SVI) has been gaining currency, driven by the rapid expansion in coverage and advances in computer vision (CV), but it has not been used much for generating 3D city models. Traditional approaches that can use SVI for reconstruction require multiple images, while in practice, often only few street-level images provide an unobstructed view of a building. We develop the reconstruction of 3D building models from a single street view image using image-to-mesh reconstruction techniques modified from the CV domain. We regard three scenarios: (1) standalone single-view reconstruction; (2) reconstruction aided by a top view delineating the footprint; and (3) refinement of existing 3D models, i.e. we examine the use of SVI to enhance the level of detail of block (LoD1) models, which are common. The results suggest that trained models supporting (2) and (3) are able to reconstruct the overall geometry of a building, while the first scenario may derive the approximate mass of the building, useful to infer the urban form of cities. We evaluate the results by demonstrating their usefulness for volume estimation, with mean errors of less than 10% for the last two scenarios. As SVI is now available in most countries worldwide, including many regions that do not have existing footprint and/or 3D building data, our method can derive rapidly and cost-effectively the 3D urban form from SVI without requiring any existing building information. Obtaining 3D building models in regions that hitherto did not have any, may enable a number of 3D geospatial analyses locally for the first time.

Improved Iterative Closest Point(ICP) 3D point cloud registration algorithm based on point cloud filtering and adaptive fireworks for coarse registration

ABSTRACTAiming at the problem of long computation time and poor registration accuracy in the current three-dimensional point cloud registration problem, this paper presents a k-dimensional Tree(KD-tree) improved ICP algorithm(KD-tree_ICP) that combines point cloud filtering and adaptive fireworks algorithms for coarse registration. On the basis of the typical KD-tree improved ICP algorithm, the point cloud filtering process and adaptive firework coarse registration process are added. Firstly, the point cloud data acquired by the 3D laser scanner is filtered. And then the adaptive fireworks algorithm is used to perform coarse registration on the filtered point cloud data. Next, the KD-tree_ICP algorithm is used to perform accurate registration on the basis of coarse registration, and the obtained translation and rotation relations are applied to the original point cloud data to obtain the result after registration. Finally, 3D point clouds of physical models of five statues are used for experimental verification, including error analysis, stability analysis and comparison with other algorithms. The experimental results show that the method proposed in this paper has greatly improved the calculation speed and accuracy, and the algorithm is stable and reliable, which can also be applied to the reconstruction of 3D building models, restoration of cultural relics, precision machining and other fields.

A multi-UAV cooperative route planning methodology for 3D fine-resolution building model reconstruction

In order to provide a fast multi-UAV cooperative data acquisition approach for 3D building model reconstruction in emergency management domain, a route planning methodology is proposed. A minimum image set including camera shooting positions and attitudes can be firstly obtained, with the given parameters describing the target building, UAVs, cameras, and image overlap requirements. A specific flight route network is then determined, and the optimal solution for multi-UAV data capture route planning is computed on the basis of constraint conditions such as the time frame, UAV battery endurance, and take-off and landing positions. Furthermore, field experiments with manual operating UAV mode, single UAV mode, and multi-UAV mode were conducted to compare the data collection and processing runtimes, as well as the quality of created 3D building models. According to the five defined LoDs of OGC CityGML 2.0 standard, the fine 3D building models conform to the LoD3. Comparison results demonstrate that our method is able to greatly enhance the efficiency of 3D reconstruction by improving the data collection speed while minimizing redundant image datasets, as well as to provide a normalized approach to assign the single or multi-UAV data acquisition tasks. The quality analysis of 3D models shows that the metric difference is less than 20 cm mean error with a standard deviation of 11 cm, which is fairly acceptable in emergency management study field. A 3D GIS-based software demo was also implemented to enable route planning, flight simulation, and data collection visualization.

Smart city: automatic reconstruction of 3D building models to support urban development and planning

The spread of smart city technologies dictates the need to develop new methods in the field of urban planning and design. The article devotes to the question how to find new approaches to make digital urban project according to the BIMtechnology. We analyze the opportunities of automated design technology BuildingReconstruction 2018 that allows constructing 3D models of landscapes, urban territories and infrastructure objects. Their distinctive feature is their high accuracy of details. We consider methods and tools for creating such models. There is the description of a 3d-model and composing information layers. On the base of the analysis of the model–s capabilities, we offer various directions how to use them for solving urban planning problems addressed to the examples of their existing implementation.

Semantically Enhanced 3D Building Model Reconstruction from Terrestrial Laser-Scanning Data

AbstractIn recent years, three-dimensional (3D) models have been used in a large variety of applications, and the steadily growing capacity in both quality and quantity is increasing demand. To app...

HRTT: A Hierarchical Roof Topology Structure for Robust Building Roof Reconstruction from Point Clouds

The identification and representation of building roof topology are basic, but important, issues for 3D building model reconstruction from point clouds. Always, the roof topology is expressed by the roof topology graph (RTG), which stores the plane–plane adjacencies as graph edges. As the decision of the graph edges is often based on local statistics between adjacent planes, topology errors can be easily produced because of noise, lack of data, and resulting errors in pre-processing steps. In this work, the hierarchical roof topology tree (HRTT) is proposed, instead of traditional RTG, to represent the topology relationships among different roof elements. Building primitives or child structures are taken as inside tree nodes; thus, the plane–model and model–model relations can be well described and further exploited. Integral constraints and extra verifying procedures can also be easily introduced to improve the topology quality. As for the basic plane-to-plane adjacencies, we no longer decide all connections at the same time, but rather we decide the robust ones preferentially. Those robust connections will separate the whole model into simpler components step-by-step and produce the basic semantic information for the identification of ambiguous ones. In this way, the effects from structures of minor importance or spurious ridges can be limited to the building locale, while the common features can be detected integrally. Experiments on various data show that the proposed method can obviously improve the topology quality and produce more precise results. Compared with the RTG-based method, two topology quality indices increase from 80.9% and 79.8% to 89.4% and 88.2% in the test area. The integral model quality indices at the pixel level and the plane level also achieve the precision of 90.3% and 84.7%, accordingly.

Implicit Regularization for Reconstructing 3D Building Rooftop Models Using Airborne LiDAR Data.

With rapid urbanization, highly accurate and semantically rich virtualization of building assets in 3D become more critical for supporting various applications, including urban planning, emergency response and location-based services. Many research efforts have been conducted to automatically reconstruct building models at city-scale from remotely sensed data. However, developing a fully-automated photogrammetric computer vision system enabling the massive generation of highly accurate building models still remains a challenging task. One the most challenging task for 3D building model reconstruction is to regularize the noises introduced in the boundary of building object retrieved from a raw data with lack of knowledge on its true shape. This paper proposes a data-driven modeling approach to reconstruct 3D rooftop models at city-scale from airborne laser scanning (ALS) data. The focus of the proposed method is to implicitly derive the shape regularity of 3D building rooftops from given noisy information of building boundary in a progressive manner. This study covers a full chain of 3D building modeling from low level processing to realistic 3D building rooftop modeling. In the element clustering step, building-labeled point clouds are clustered into homogeneous groups by applying height similarity and plane similarity. Based on segmented clusters, linear modeling cues including outer boundaries, intersection lines, and step lines are extracted. Topology elements among the modeling cues are recovered by the Binary Space Partitioning (BSP) technique. The regularity of the building rooftop model is achieved by an implicit regularization process in the framework of Minimum Description Length (MDL) combined with Hypothesize and Test (HAT). The parameters governing the MDL optimization are automatically estimated based on Min-Max optimization and Entropy-based weighting method. The performance of the proposed method is tested over the International Society for Photogrammetry and Remote Sensing (ISPRS) benchmark datasets. The results show that the proposed method can robustly produce accurate regularized 3D building rooftop models.

A Graph-Based Approach for 3D Building Model Reconstruction from Airborne LiDAR Point Clouds

3D building model reconstruction is of great importance for environmental and urban applications. Airborne light detection and ranging (LiDAR) is a very useful data source for acquiring detailed geometric and topological information of building objects. In this study, we employed a graph-based method based on hierarchical structure analysis of building contours derived from LiDAR data to reconstruct urban building models. The proposed approach first uses a graph theory-based localized contour tree method to represent the topological structure of buildings, then separates the buildings into different parts by analyzing their topological relationships, and finally reconstructs the building model by integrating all the individual models established through the bipartite graph matching process. Our approach provides a more complete topological and geometrical description of building contours than existing approaches. We evaluated the proposed method by applying it to the Lujiazui region in Shanghai, China, a complex and large urban scene with various types of buildings. The results revealed that complex buildings could be reconstructed successfully with a mean modeling error of 0.32 m. Our proposed method offers a promising solution for 3D building model reconstruction from airborne LiDAR point clouds.

3D Building Reconstruction Using Dense Photogrammetric Point Cloud

Three dimensional models of urban areas play an important role in city planning, disaster management, city navigation and other applications. Reconstruction of 3D building models is still a challenging issue in 3D city modelling. Point clouds generated from multi view images of UAV is a novel source of spatial data, which is used in this research for building reconstruction. The process starts with the segmentation of point clouds of roofs and walls into planar groups. By generating related surfaces and using geometrical constraints plus considering symmetry, a 3d model of building is reconstructed. In a refinement step, dormers are extracted, and their models are reconstructed. The details of the 3d reconstructed model are in LoD3 level, with respect to modelling eaves, fractions of roof and dormers.

3D Building Reconstruction Using Dense Photogrammetric Point Cloud

Abstract. Three dimensional models of urban areas play an important role in city planning, disaster management, city navigation and other applications. Reconstruction of 3D building models is still a challenging issue in 3D city modelling. Point clouds generated from multi view images of UAV is a novel source of spatial data, which is used in this research for building reconstruction. The process starts with the segmentation of point clouds of roofs and walls into planar groups. By generating related surfaces and using geometrical constraints plus considering symmetry, a 3d model of building is reconstructed. In a refinement step, dormers are extracted, and their models are reconstructed. The details of the 3d reconstructed model are in LoD3 level, with respect to modelling eaves, fractions of roof and dormers.

JOINT SIMULTANEOUS RECONSTRUCTION OF REGULARIZED BUILDING SUPERSTRUCTURES FROM LOW-DENSITY LIDAR DATA USING ICP

Abstract. There are many applications for 3D city models, e.g., in visualizations, analysis, and simulations; each one requiring a certain level of detail to be effective. The overall trend goes towards including various kinds of anthropogenic and natural objects therein with ever increasing geometric and semantic details. A few years back, the featured 3D building models had only coarse roof geometry. But nowadays, they are expected to include detailed roof superstructures like dormers and chimneys. Several methods have been proposed for the automatic reconstruction of 3D building models from airborne based point clouds. However, they are usually unable to reliably recognize and reconstruct small roof superstructures as these objects are often represented by only few point measurements, especially in low-density point clouds. In this paper, we propose a recognition and reconstruction approach that overcomes this problem by identifying and simultaneously reconstructing regularized superstructures of similar shape. For this purpose, candidate areas for superstructures are detected by taking into account virtual sub-surface points that are assumed to lie on the main roof faces below the measured points. The areas with similar superstructures are detected, extracted, grouped together, and registered to one another with the Iterative Closest Point (ICP) algorithm. As an outcome, the joint point density of each detected group is increased, which helps to recognize the shape of the superstructure more reliably and in more detail. Finally, all instances of each group of superstructures are modeled at once and transformed back to their original position. Because superstructures are reconstructed in groups, symmetries, alignments, and regularities can be enforced in a straight-forward way. The validity of the approach is presented on a number of example buildings from the Vaihingen test data set.

JOINT SIMULTANEOUS RECONSTRUCTION OF REGULARIZED BUILDING SUPERSTRUCTURES FROM LOW-DENSITY LIDAR DATA USING ICP

There are many applications for 3D city models, e.g., in visualizations, analysis, and simulations; each one requiring a certain level of detail to be effective. The overall trend goes towards including various kinds of anthropogenic and natural objects therein with ever increasing geometric and semantic details. A few years back, the featured 3D building models had only coarse roof geometry. But nowadays, they are expected to include detailed roof superstructures like dormers and chimneys. Several methods have been proposed for the automatic reconstruction of 3D building models from airborne based point clouds. However, they are usually unable to reliably recognize and reconstruct small roof superstructures as these objects are often represented by only few point measurements, especially in low-density point clouds. In this paper, we propose a recognition and reconstruction approach that overcomes this problem by identifying and simultaneously reconstructing regularized superstructures of similar shape. For this purpose, candidate areas for superstructures are detected by taking into account virtual sub-surface points that are assumed to lie on the main roof faces below the measured points. The areas with similar superstructures are detected, extracted, grouped together, and registered to one another with the Iterative Closest Point (ICP) algorithm. As an outcome, the joint point density of each detected group is increased, which helps to recognize the shape of the superstructure more reliably and in more detail. Finally, all instances of each group of superstructures are modeled at once and transformed back to their original position. Because superstructures are reconstructed in groups, symmetries, alignments, and regularities can be enforced in a straight-forward way. The validity of the approach is presented on a number of example buildings from the Vaihingen test data set.

Automatic reconstruction of 3D building models from scanned 2D floor plans

The overall energy efficiency of existing buildings has to be significantly improved to comply with emerging regulations and to contribute to overcoming current environmental challenges. Many policies aim at accelerating the renovation rate. The effectiveness of renovation actions could be significantly improved through the systematic use of Information and Communication Technologies (ICT) tools and Building Information Modeling (BIM). But these solutions rely on full-fledged digital models, which, for most buildings, are not available. The present article introduces a research work aiming at the development of methods for the generation of 3D building models from 2D plans. The developed prototype is able to extract information from 2D plans and to generate IFC (Industry Foundation Classes)-compliant 3D models that include the main components of the building: walls, openings, and spaces. The article also presents the results of a quantitative assessment of the platform capabilities and performances, relying on a database of 90 real architectural floor plans. The results are very promising and show that such solutions could be key components of future digital toolkits for renovation design.

INTEGRATION OF BUILDING KNOWLEDGE INTO BINARY SPACE PARTITIONING FOR THE RECONSTRUCTION OF REGULARIZED BUILDING MODELS

Abstract. Recent approaches for the automatic reconstruction of 3D building models from airborne point cloud data integrate prior knowledge of roof shapes with the intention to improve the regularization of the resulting models without lessening the flexibility to generate all real-world occurring roof shapes. In this paper, we present a method to integrate building knowledge into the data-driven approach that uses binary space partitioning (BSP) for modeling the 3D building geometry. A retrospective regularization of polygons that emerge from the BSP tree is not without difficulty because it has to deal with the 2D BSP subdivision itself and the plane definitions of the resulting partition regions to ensure topological correctness. This is aggravated by the use of hyperplanes during the binary subdivision that often splits planar roof regions into several parts that are stored in different subtrees of the BSP tree. We therefore introduce the use of hyperpolylines in the generation of the BSP tree to avoid unnecessary spatial subdivisions, so that the spatial integrity of planar roof regions is better maintained. The hyperpolylines are shown to result from basic building roof knowledge that is extracted based on roof topology graphs. An adjustment of the underlying point segments ensures that the positions of the extracted hyperpolylines result in regularized 2D partitions as well as topologically correct 3D building models. The validity and limitations of the approach are demonstrated on real-world examples.

Building Roof Component Extraction from Panchromatic Satellite Images Using a Clustering-Based Method

Abstract. Developing fully automatic systems is still an active research topic in 3D building model reconstruction. While a general solution to the building reconstruction problem relies on collecting and grouping the modeling cues (e.g., lines, corners, planes) from Digital Surface Model (DSM) data, failure in finding the cues due to noise in the DSM and the object complexities is a big challenge. In this paper, we introduce a clustering-based method for cue discovery from Pan-chromatic satellite images which reduces the dependencies of the reconstruction techniques on DSM data. Experimental results show that the proposed method is not only able to effectively refine building masks by discriminating building boundaries from nearby clutter, but also is able to determine the roof types (e.g., pitched, flat). The latter, allows to establish a reconstruction method to reduces the search effort and the failure probability regions in finding a particular cue by leading the system to an appropriate area.

3D Building Adjustment Using Planar Half-Space Regularities

Abstract. The automatic reconstruction of 3D building models with complex roof shapes is still an active area of research. In this paper we present a novel approach for local and global regularization rules that integrate building knowledge to improve both the shape of the reconstructed building models and their accuracy. These rules are defined for the planar half-space representation of our models and emphasize the presence of symmetries, co-planarity, parallelism, and orthogonality. By not adjusting building features separately (e.g. ridges, eaves, etc.) we are able to handle more than one feature at a time without considering dependencies between different features. Additionally, we present a new method for reconstructing buildings with concave outlines using half-spaces that avoids the need to partition the models into smaller convex parts. We present both extensions in the context of a fully automatic feature-driven 3D building reconstruction approach where the whole process is suited for processing large urban areas with complex building roofs.

A graph edit dictionary for correcting errors in roof topology graphs reconstructed from point clouds

In the task of 3D building model reconstruction from point clouds we face the problem of recovering a roof topology graph in the presence of noise, small roof faces and low point densities. Errors in roof topology graphs will seriously affect the final modelling results. The aim of this research is to automatically correct these errors. We define the graph correction as a graph-to-graph problem, similar to the spelling correction problem (also called the string-to-string problem). The graph correction is more complex than string correction, as the graphs are 2D while strings are only 1D. We design a strategy based on a dictionary of graph edit operations to automatically identify and correct the errors in the input graph. For each type of error the graph edit dictionary stores a representative erroneous subgraph as well as the corrected version. As an erroneous roof topology graph may contain several errors, a heuristic search is applied to find the optimum sequence of graph edits to correct the errors one by one. The graph edit dictionary can be expanded to include entries needed to cope with errors that were previously not encountered. Experiments show that the dictionary with only fifteen entries already properly corrects one quarter of erroneous graphs in about 4500 buildings, and even half of the erroneous graphs in one test area, achieving as high as a 95% acceptance rate of the reconstructed models.