Automatic co-registration of photogrammetric point clouds with digital building models

In a post-earthquake scenario, the semantic enrichment of 3D building models with seismic damage is crucial from the perspective of disaster management. This paper aims to present the methodology and the results for the Level of Detail 3 (LOD3) building modelling (after an earthquake) with the enrichment of the semantics of the seismic damage based on the European Macroseismic Scale (EMS-98). The study area is the Vrisa traditional settlement on the island of Lesvos, Greece, which was affected by a devastating earthquake of Mw = 6.3 on 12 June 2017. The applied methodology consists of the following steps: (a) unmanned aircraft systems (UAS) nadir and oblique images are acquired and photogrammetrically processed for 3D point cloud generation, (b) 3D building models are created based on 3D point clouds and (c) 3D building models are transformed into a LOD3 City Geography Markup Language (CityGML) standard with enriched semantics of the related seismic damage of every part of the building (walls, roof, etc.). The results show that in following this methodology, CityGML LOD3 models can be generated and enriched with buildings’ seismic damage. These models can assist in the decision-making process during the recovery phase of a settlement as well as be the basis for its monitoring over time. Finally, these models can contribute to the estimation of the reconstruction cost of the buildings.

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Towards Reconstructing 3D Buildings from ALS Data Based on Gestalt Laws

Remote Sensing ◽

10.3390/rs10071127 ◽

2018 ◽

Vol 10 (7) ◽

pp. 1127 ◽

Cited By ~ 5

Author(s):

Pingbo Hu ◽

Bisheng Yang ◽

Zhen Dong ◽

Pengfei Yuan ◽

Ronggang Huang ◽

...

Keyword(s):

Structural Model ◽

Point Clouds ◽

Complex Nature ◽

Building Structures ◽

Data Infrastructure ◽

Roof Structure ◽

Building Models ◽

Topological Tree ◽

City System ◽

Gestalt Laws

3D building models are an essential data infrastructure for various applications in a smart city system, since they facilitate spatial queries, spatial analysis, and interactive visualization. Due to the highly complex nature of building structures, automatically reconstructing 3D buildings from point clouds remains a challenging task. In this paper, a Roof Attribute Graph (RAG) method is proposed to describe the decomposition and topological relations within a complicated roof structure. Furthermore, top-down decomposition and bottom-up refinement processes are proposed to reconstruct roof parts according to the Gestalt laws, generating a complete structural model with a hierarchical topological tree. Two LiDAR datasets from Guangdong (China) and Vaihingen (Germany) with different point densities were used in our study. Experimental results, including the assessment on Vaihingen standardized by the International Society for Photogrammetry and Remote Sensing (ISPRS), show that the proposed method can be used to model 3D building roofs with high quality results as demonstrated by the completeness and correctness metrics presented in this paper.

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ANALYSIS OF 3D BUILDING MODELS ACCURACY BASED ON THE AIRBORNE LASER SCANNING POINT CLOUDS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-797-2018 ◽

2018 ◽

Vol XLII-2 ◽

pp. 797-804 ◽

Cited By ~ 4

Author(s):

W. Ostrowski ◽

M. Pilarska ◽

J. Charyton ◽

K. Bakuła

Keyword(s):

Point Cloud ◽

Laser Scanning ◽

Large Scale ◽

Point Clouds ◽

3D Models ◽

Airborne Laser Scanning ◽

Aerial Images ◽

Statistical Parameters ◽

Airborne Laser ◽

Building Models

Creating 3D building models in large scale is becoming more popular and finds many applications. Nowadays, a wide term “3D building models” can be applied to several types of products: well-known CityGML solid models (available on few Levels of Detail), which are mainly generated from Airborne Laser Scanning (ALS) data, as well as 3D mesh models that can be created from both nadir and oblique aerial images. City authorities and national mapping agencies are interested in obtaining the 3D building models. Apart from the completeness of the models, the accuracy aspect is also important. Final accuracy of a building model depends on various factors (accuracy of the source data, complexity of the roof shapes, etc.). In this paper the methodology of inspection of dataset containing 3D models is presented. The proposed approach check all building in dataset with comparison to ALS point clouds testing both: accuracy and level of details. Using analysis of statistical parameters for normal heights for reference point cloud and tested planes and segmentation of point cloud provides the tool that can indicate which building and which roof plane in do not fulfill requirement of model accuracy and detail correctness. Proposed method was tested on two datasets: solid and mesh model.

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HOLISTIC PARAMETRIC RECONSTRUCTION OF BUILDING MODELS FROM POINT CLOUDS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b2-2020-689-2020 ◽

2020 ◽

Vol XLIII-B2-2020 ◽

pp. 689-695

Author(s):

Z. Li ◽

W. Zhang ◽

J. Shan

Keyword(s):

Large Scale ◽

Traditional Approach ◽

Holistic Approach ◽

Point Clouds ◽

Airborne Lidar ◽

Reconstruction Method ◽

Optimization Strategy ◽

Building Roof ◽

Building Model ◽

Building Models

Abstract. Building models are conventionally reconstructed by building roof points via planar segmentation and then using a topology graph to group the planes together. Roof edges and vertices are then mathematically represented by intersecting segmented planes. Technically, such solution is based on sequential local fitting, i.e., the entire data of one building are not simultaneously participating in determining the building model. As a consequence, the solution is lack of topological integrity and geometric rigor. Fundamentally different from this traditional approach, we propose a holistic parametric reconstruction method which means taking into consideration the entire point clouds of one building simultaneously. In our work, building models are reconstructed from predefined parametric (roof) primitives. We first use a well-designed deep neural network to segment and identify primitives in the given building point clouds. A holistic optimization strategy is then introduced to simultaneously determine the parameters of a segmented primitive. In the last step, the optimal parameters are used to generate a watertight building model in CityGML format. The airborne LiDAR dataset RoofN3D with predefined roof types is used for our test. It is shown that PointNet++ applied to the entire dataset can achieve an accuracy of 83% for primitive classification. For a subset of 910 buildings in RoofN3D, the holistic approach is then used to determine the parameters of primitives and reconstruct the buildings. The achieved overall quality of reconstruction is 0.08 meters for point-surface-distance or 0.7 times RMSE of the input LiDAR points. This study demonstrates the efficiency and capability of the proposed approach and its potential to handle large scale urban point clouds.

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Automated Storey Separation and Door and Window Extraction for Building Models from Complete Laser Scans

Remote Sensing ◽

10.3390/rs13173384 ◽

2021 ◽

Vol 13 (17) ◽

pp. 3384

Author(s):

Kate Pexman ◽

Derek D. Lichti ◽

Peter Dawson

Keyword(s):

Building Design ◽

Point Clouds ◽

Heritage Sites ◽

Building Models ◽

Significant Research ◽

Heritage Buildings ◽

Floor Plans ◽

Multi Level ◽

Dimensional Errors ◽

Work Done

Heritage buildings are often lost without being adequately documented. Significant research has gone into automated building modelling from point clouds, challenged by irregularities in building design and the presence of occlusion-causing clutter and non-Manhattan World features. Previous work has been largely focused on the extraction and representation of walls, floors, and ceilings from either interior or exterior single storey scans. Significantly less effort has been concentrated on the automated extraction of smaller features such as windows and doors from complete (interior and exterior) scans. In addition, the majority of the work done on automated building reconstruction pertains to the new-build and construction industries, rather than for heritage buildings. This work presents a novel multi-level storey separation technique as well as a novel door and window detection strategy within an end-to-end modelling software for the automated creation of 2D floor plans and 3D building models from complete terrestrial laser scans of heritage buildings. The methods are demonstrated on three heritage sites of varying size and complexity, achieving overall accuracies of 94.74% for multi-level storey separation and 92.75% for the building model creation. Additionally, the automated door and window detection methodology achieved absolute mean dimensional errors of 6.3 cm.

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Building modeling from noisy photogrammetric point clouds

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsannals-ii-3-197-2014 ◽

2014 ◽

Vol II-3 ◽

pp. 197-204 ◽

Cited By ~ 8

Author(s):

B. Xiong ◽

S. Oude Elberink ◽

G. Vosselman

Keyword(s):

Point Cloud ◽

Point Clouds ◽

Good Alternative ◽

Building Structures ◽

The Novel ◽

Model Driven ◽

Building Models ◽

Building Modeling ◽

Fully Automatic ◽

High Level

The Multi-View Stereo (MVS) technology has improved significantly in the last decade, providing a much denser and more accurate point cloud than before. The point cloud now becomes a valuable data for modelling the LOD2 buildings. However, it is still not accurate enough to replace the lidar point cloud. Its relative high level of noise prevents the accurate interpretation of roof faces, e.g. one planar roof face has uneven surface of points therefore is segmented into many parts. The derived roof topology graphs are quite erroneous and cannot be used to model the buildings using the current methods based on roof topology graphs. We propose a parameter-free algorithm to robustly and precisely derive roof structures and building models. The points connecting roof segments are searched and grouped as structure points and structure boundaries, accordingly presenting the roof corners and boundaries. Their geometries are computed by the plane equations of their attached roof segments. If data available, the algorithm guarantees complete building structures in noisy point clouds and meanwhile achieves global optimized models. Experiments show that, when comparing to the roof topology graph based methods, the novel algorithm achieves consistent quality for both lidar and photogrammetric point clouds. But the new method is fully automatic and is a good alternative for the model-driven method when the processing time is important.

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FIRST PRISMATIC BUILDING MODEL RECONSTRUCTION FROM TOMOSAR POINT CLOUDS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b3-381-2016 ◽

2016 ◽

Vol XLI-B3 ◽

pp. 381-386 ◽

Cited By ~ 1

Author(s):

Y. Sun ◽

M. Shahzad ◽

X. Zhu

Keyword(s):

Point Clouds ◽

Denoising Method ◽

Building Model ◽

Line Simplification ◽

Building Models ◽

Convention Center ◽

The Individual ◽

First Time ◽

The City ◽

Large Building

This paper demonstrates for the first time the potential of explicitly modelling the individual roof surfaces to reconstruct 3-D prismatic building models using spaceborne tomographic synthetic aperture radar (TomoSAR) point clouds. The proposed approach is modular and works as follows: it first extracts the buildings via DSM generation and cutting-off the ground terrain. The DSM is smoothed using BM3D denoising method proposed in (Dabov et al., 2007) and a gradient map of the smoothed DSM is generated based on height jumps. Watershed segmentation is then adopted to oversegment the DSM into different regions. Subsequently, height and polygon complexity constrained merging is employed to refine (i.e., to reduce) the retrieved number of roof segments. Coarse outline of each roof segment is then reconstructed and later refined using quadtree based regularization plus zig-zag line simplification scheme. Finally, height is associated to each refined roof segment to obtain the 3-D prismatic model of the building. The proposed approach is illustrated and validated over a large building (convention center) in the city of Las Vegas using TomoSAR point clouds generated from a stack of 25 images using Tomo-GENESIS software developed at DLR.

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FAÇADE RECONSTRUCTION USING GEOMETRIC AND RADIOMETRIC POINT CLOUD INFORMATION

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-3-w2-247-2015 ◽

2015 ◽

Vol XL-3/W2 ◽

pp. 247-252 ◽

Cited By ~ 8

Author(s):

P. Tutzauer ◽

N. Haala

Keyword(s):

Point Cloud ◽

Point Clouds ◽

Building Models ◽

Geometric Reconstruction ◽

Dense Image ◽

Local Point ◽

Segmentation Approach ◽

Geometric Segmentation ◽

Mobile Mapping Systems ◽

Limited Accuracy

This paper aims at façade reconstruction for subsequent enrichment of LOD2 building models. We use point clouds from dense image matching with imagery both from Mobile Mapping systems and oblique airborne cameras. The interpretation of façade structures is based on a geometric reconstruction. For this purpose a pre-segmentation of the point cloud into façade points and non-façade points is necessary. We present an approach for point clouds with limited geometric accuracy where a geometric segmentation might fail. Our contribution is a radiometric segmentation approach. Via local point features, based on a clustering in hue space, the point cloud is segmented into façade-points and non-façade points. This way, the initial geometric reconstruction step can be bypassed and point clouds with limited accuracy can still serve as input for the façade reconstruction and modelling approach.

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AUTOMATIC GENERATION OF BUILDING MODELS WITH LEVELS OF DETAIL 1-3

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b3-649-2016 ◽

2016 ◽

Vol XLI-B3 ◽

pp. 649-654 ◽

Cited By ~ 2

Author(s):

W. Nguatem ◽

M. Drauschke ◽

H. Mayer

Keyword(s):

Model Selection ◽

Point Clouds ◽

Automatic Generation ◽

Stochastic Method ◽

Depth Maps ◽

Levels Of Detail ◽

3D Point Clouds ◽

Building Models ◽

Global Matching

We present a workflow for the automatic generation of building models with levels of detail (LOD) 1 to 3 according to the CityGML standard (Gröger et al., 2012). We start with orienting unsorted image sets employing (Mayer et al., 2012), we compute depth maps using semi-global matching (SGM) (Hirschmüller, 2008), and fuse these depth maps to reconstruct dense 3D point clouds (Kuhn et al., 2014). Based on planes segmented from these point clouds, we have developed a stochastic method for roof model selection (Nguatem et al., 2013) and window model selection (Nguatem et al., 2014). We demonstrate our workflow up to the export into CityGML.

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Fusion of Multi-Sensor-Derived Heights and OSM-Derived Building Footprints for Urban 3D Reconstruction

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8040193 ◽

2019 ◽

Vol 8 (4) ◽

pp. 193 ◽

Cited By ~ 4

Author(s):

Hossein Bagheri ◽

Michael Schmitt ◽

Xiaoxiang Zhu

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Point Clouds ◽

Original Data ◽

Markup Language ◽

Geography Markup Language ◽

Building Models ◽

Extensive Coverage ◽

Constant Height ◽

Digital Elevation

So-called prismatic 3D building models, following the level-of-detail (LOD) 1 of the OGC City Geography Markup Language (CityGML) standard, are usually generated automatically by combining building footprints with height values. Typically, high-resolution digital elevation models (DEMs) or dense LiDAR point clouds are used to generate these building models. However, high-resolution LiDAR data are usually not available with extensive coverage, whereas globally available DEM data are often not detailed and accurate enough to provide sufficient input to the modeling of individual buildings. Therefore, this paper investigates the possibility of generating LOD1 building models from both volunteered geographic information (VGI) in the form of OpenStreetMap data and remote sensing-derived geodata improved by multi-sensor and multi-modal DEM fusion techniques or produced by synthetic aperture radar (SAR)-optical stereogrammetry. The results of this study show several things: First, it can be seen that the height information resulting from data fusion is of higher quality than the original data sources. Secondly, the study confirms that simple, prismatic building models can be reconstructed by combining OpenStreetMap building footprints and easily accessible, remote sensing-derived geodata, indicating the potential of application on extensive areas. The building models were created under the assumption of flat terrain at a constant height, which is valid in the selected study area.

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