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

2016 ◽  
Vol XLI-B3 ◽  
pp. 649-654 ◽  
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.

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

2016 ◽  
Vol XLI-B3 ◽  
pp. 649-654
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.

scholarly journals Seismic Damage Semantics on Post-Earthquake LOD3 Building Models Generated by UAS

2021 ◽  
Vol 10 (5) ◽  
pp. 345
Author(s):  
Konstantinos Chaidas ◽  
George Tataris ◽  
Nikolaos Soulakellis
Keyword(s):  
Point Clouds ◽  
Recovery Phase ◽  
Seismic Damage ◽  
Unmanned Aircraft ◽  
Earthquake Scenario ◽  
Geography Markup Language ◽  
3D Point Clouds ◽  
Building Models ◽  
Building Walls ◽  
Oblique Images

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.

scholarly journals FAST PROBABILISTIC FUSION OF 3D POINT CLOUDS VIA OCCUPANCY GRIDS FOR SCENE CLASSIFICATION

2016 ◽  
Vol III-3 ◽  
pp. 325-332
Author(s):  
Andreas Kuhn ◽  
Hai Huang ◽  
Martin Drauschke ◽  
Helmut Mayer
Keyword(s):  
High Resolution ◽  
Point Cloud ◽  
State Of The Art ◽  
Point Clouds ◽  
Automatic Generation ◽  
Scene Classification ◽  
Semantic Classification ◽  
3D Point Clouds ◽  
Occupancy Grids ◽  
High Scalability

High resolution consumer cameras on Unmanned Aerial Vehicles (UAVs) allow for cheap acquisition of highly detailed images, e.g., of urban regions. Via image registration by means of Structure from Motion (SfM) and Multi View Stereo (MVS) the automatic generation of huge amounts of 3D points with a relative accuracy in the centimeter range is possible. Applications such as semantic classification have a need for accurate 3D point clouds, but do not benefit from an extremely high resolution/density. In this paper, we, therefore, propose a fast fusion of high resolution 3D point clouds based on occupancy grids. The result is used for semantic classification. In contrast to state-of-the-art classification methods, we accept a certain percentage of outliers, arguing that they can be considered in the classification process when a per point belief is determined in the fusion process. To this end, we employ an octree-based fusion which allows for the derivation of outlier probabilities. The probabilities give a belief for every 3D point, which is essential for the semantic classification to consider measurement noise. For an example point cloud with half a billion 3D points (cf. Figure 1), we show that our method can reduce runtime as well as improve classification accuracy and offers high scalability for large datasets.

scholarly journals AUTOMATED MOSAICKING OF MULTIPLE 3D POINT CLOUDS GENERATED FROM A DEPTH CAMERA

2016 ◽  
Vol XLI-B3 ◽  
pp. 269-272
Author(s):  
H. Kim ◽  
W. Yoon ◽  
T. Kim
Keyword(s):  
Depth Map ◽  
Point Clouds ◽  
Mapping Method ◽  
Transformation Model ◽  
Depth Camera ◽  
Depth Data ◽  
Depth Maps ◽  
3D Point Clouds ◽  
Intensity Map ◽  
Time Of Flight Method

In this paper, we propose a method for automated mosaicking of multiple 3D point clouds generated from a depth camera. A depth camera generates depth data by using ToF (Time of Flight) method and intensity data by using intensity of returned signal. The depth camera used in this paper was a SR4000 from MESA Imaging. This camera generates a depth map and intensity map of 176 x 44 pixels. Generated depth map saves physical depth data with mm of precision. Generated intensity map contains texture data with many noises. We used texture maps for extracting tiepoints and depth maps for assigning z coordinates to tiepoints and point cloud mosaicking. There are four steps in the proposed mosaicking method. In the first step, we acquired multiple 3D point clouds by rotating depth camera and capturing data per rotation. In the second step, we estimated 3D-3D transformation relationships between subsequent point clouds. For this, 2D tiepoints were extracted automatically from the corresponding two intensity maps. They were converted into 3D tiepoints using depth maps. We used a 3D similarity transformation model for estimating the 3D-3D transformation relationships. In the third step, we converted local 3D-3D transformations into a global transformation for all point clouds with respect to a reference one. In the last step, the extent of single depth map mosaic was calculated and depth values per mosaic pixel were determined by a ray tracing method. For experiments, 8 depth maps and intensity maps were used. After the four steps, an output mosaicked depth map of 454x144 was generated. It is expected that the proposed method would be useful for developing an effective 3D indoor mapping method in future.

scholarly journals Recognition of Human Activities Using Depth Maps and the Viewpoint Feature Histogram Descriptor

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2940 ◽  
Author(s):  
Kamil Sidor ◽  
Marian Wysocki
Keyword(s):  
Human Activities ◽  
Short Term Memory ◽  
Point Clouds ◽  
Human Action ◽  
Learning Networks ◽  
University Of Texas ◽  
Multiple Networks ◽  
Depth Maps ◽  
3D Point Clouds ◽  
Dynamic Time

In this paper we propose a way of using depth maps transformed into 3D point clouds to classify human activities. The activities are described as time sequences of feature vectors based on the Viewpoint Feature Histogram descriptor (VFH) computed using the Point Cloud Library. Recognition is performed by two types of classifiers: (i) k-NN nearest neighbors’ classifier with Dynamic Time Warping measure, (ii) bidirectional long short-term memory (BiLSTM) deep learning networks. Reduction of classification time for the k-NN by introducing a two tier model and improvement of BiLSTM-based classification via transfer learning and combining multiple networks by fuzzy integral are discussed. Our classification results obtained on two representative datasets: University of Texas at Dallas Multimodal Human Action Dataset and Mining Software Repositories Action 3D Dataset are comparable or better than the current state of the art.

scholarly journals AN EFFICIENT REPRESENTATION OF 3D BUILDINGS: APPLICATION TO THE EVALUATION OF CITY MODELS

2021 ◽  
Vol XLIII-B2-2021 ◽  
pp. 329-336
Author(s):  
O. Ennafii ◽  
A. Le Bris ◽  
F. Lafarge ◽  
C. Mallet
Keyword(s):  
Evaluation Process ◽  
Point Clouds ◽  
Classification Problem ◽  
Boundary Representation ◽  
3D Point Clouds ◽  
Learning Tasks ◽  
Building Models ◽  
Urban Models ◽  
Efficient Representation ◽  
Building Level

Abstract. City modeling consists in building a semantic generalized model of the surface of urban objects. These could be seen as a special case of Boundary representation surfaces. Most modeling methods focus on 3D buildings with Very High Resolution overhead data (images and/or 3D point clouds). The literature abundantly addresses 3D mesh processing but frequently ignores the analysis of such models. This requires an efficient representation of 3D buildings. In particular, for them to be used in supervised learning tasks, such a representation should be scalable and transferable to various environments as only a few reference training instances would be available. In this paper, we propose two solutions that take into account the specificity of 3D urban models. They are based on graph kernels and Scattering Network. They are here evaluated in the challenging framework of quality evaluation of building models. The latter is formulated as a supervised multilabel classification problem, where error labels are predicted at building level. The experiments show for both feature extraction strategy strong and complementary results (F-score > 74% for most labels). Transferability of the classification is also examined in order to assess the scalability of the evaluation process yielding very encouraging scores (F-score > 86% for most labels).

scholarly journals CityJSON Building Generation from Airborne LiDAR 3D Point Clouds

2020 ◽  
Vol 9 (9) ◽  
pp. 521
Author(s):  
Gilles-Antoine Nys ◽  
Florent Poux ◽  
Roland Billen
Keyword(s):  
Smart Cities ◽  
Contextual Information ◽  
Compliance Rate ◽  
Point Clouds ◽  
3D Models ◽  
Automatic Generation ◽  
Airborne Lidar ◽  
Generation Process ◽  
3D Point Clouds ◽  
Unique Source

The relevant insights provided by 3D City models greatly improve Smart Cities and their management policies. In the urban built environment, buildings frequently represent the most studied and modeled features. CityJSON format proposes a lightweight and developer-friendly alternative to CityGML. This paper proposes an improvement to the usability of 3D models providing an automatic generation method in CityJSON, to ensure compactness, expressivity, and interoperability. In addition to a compliance rate in excess of 92% for geometry and topology, the generated model allows the handling of contextual information, such as metadata and refined levels of details (LoD), in a built-in manner. By breaking down the building-generation process, it creates consistent building objects from the unique source of Light Detection and Ranging (LiDAR) point clouds.

scholarly journals From 3D Point Clouds to Pose-Normalised Depth Maps

2009 ◽  
Vol 89 (2-3) ◽  
pp. 152-176 ◽  
Author(s):  
Nick Pears ◽  
Tom Heseltine ◽  
Marcelo Romero
Keyword(s):  
Point Clouds ◽  
Depth Maps ◽  
3D Point Clouds

scholarly journals GRAMMAR-SUPPORTED 3D INDOOR RECONSTRUCTION FROM POINT CLOUDS FOR “AS-BUILT” BIM

2015 ◽  
Vol II-3/W4 ◽  
pp. 17-24 ◽  
Author(s):  
S. Becker ◽  
M. Peter ◽  
D. Fritsch
Keyword(s):  
Point Clouds ◽  
Observation Data ◽  
Reconstruction Process ◽  
Building Model ◽  
3D Point Clouds ◽  
Building Models ◽  
Seamless Transition ◽  
Floor Plans ◽  
Knowledge Content ◽  
Realistic Geometries

The paper presents a grammar-based approach for the robust automatic reconstruction of 3D interiors from raw point clouds. The core of the approach is a 3D indoor grammar which is an extension of our previously published grammar concept for the modeling of 2D floor plans. The grammar allows for the modeling of buildings whose horizontal, continuous floors are traversed by hallways providing access to the rooms as it is the case for most office buildings or public buildings like schools, hospitals or hotels. The grammar is designed in such way that it can be embedded in an iterative automatic learning process providing a seamless transition from LOD3 to LOD4 building models. Starting from an initial low-level grammar, automatically derived from the window representations of an available LOD3 building model, hypotheses about indoor geometries can be generated. The hypothesized indoor geometries are checked against observation data - here 3D point clouds - collected in the interior of the building. The verified and accepted geometries form the basis for an automatic update of the initial grammar. By this, the knowledge content of the initial grammar is enriched, leading to a grammar with increased quality. This higher-level grammar can then be applied to predict realistic geometries to building parts where only sparse observation data are available. Thus, our approach allows for the robust generation of complete 3D indoor models whose quality can be improved continuously as soon as new observation data are fed into the grammar-based reconstruction process. The feasibility of our approach is demonstrated based on a real-world example.

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