scholarly journals AUTOMATIC 3D RECONSTRUCTION OF COMPLEX BUILDINGS FROM INCOMPLETE POINT CLOUDS WITH TOPOLOGICAL-RELATION CONSTRAINTS

2020 ◽  
Vol V-5-2020 ◽  
pp. 85-92
Author(s):  
Y. Li ◽  
B. Wu
Keyword(s):  
3D Reconstruction ◽  
Laser Scanning ◽  
Large Scale ◽  
Point Clouds ◽  
3D Models ◽  
Automatic Generation ◽  
Topological Relation ◽  
Architectural Styles ◽  
Topological Errors ◽  
Polyhedral Cells

Abstract. Automatic 3D building reconstruction from laser scanning or photogrammetric point clouds has gained increasing attention in the past two decades. Although many efforts have been made, the complexity of buildings and incompletion of point clouds, i.e., data missing, still make it a challenging task for automatic 3D reconstruction of buildings in large-scale urban scenes with various architectural styles. This paper presents an innovative approach for automatic generation of 3D models of complex buildings from even incomplete point clouds. The approach first decomposes the 3D space into multiple space units, including 3D polyhedral cells, facets and edges, where the facets and edges are also encoded with topological-relation constraints. Then, the units and constraints are used together to approximate the buildings. On one hand, by extracting facets from 3D cells and further extracting edges from facets, this approach simplifies complicated topological computations. On the other hand, because this approach models buildings on the basis of polyhedral cells, it can guarantee that the models are manifold and watertight and avoid correcting topological errors. A challenging dataset containing 105 buildings acquired in Central, Hong Kong, was used to evaluate the performance of the proposed approach. The results were compared with two previous methods and the comparisons suggested that the proposed approach outperforms other methods in terms of robustness, regularity, and accuracy of the models, with an average root-mean-square error of less than 0.9 m. The proposed approach is of significance for automatic 3D modelling of buildings for urban applications.

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

2018 ◽  
Vol XLII-2 ◽  
pp. 797-804 ◽  
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.

scholarly journals Weighing trees with lasers: advances, challenges and opportunities

2018 ◽  
Vol 8 (2) ◽  
pp. 20170048 ◽  
Author(s):  
M. I. Disney ◽  
M. Boni Vicari ◽  
A. Burt ◽  
K. Calders ◽  
S. L. Lewis ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Large Scale ◽  
Three Dimensional ◽  
Point Clouds ◽  
3D Models ◽  
Tree Form ◽  
Form And Function ◽  
Challenges And Opportunities ◽  
And Function ◽  
Non Destructive

Terrestrial laser scanning (TLS) is providing exciting new ways to quantify tree and forest structure, particularly above-ground biomass (AGB). We show how TLS can address some of the key uncertainties and limitations of current approaches to estimating AGB based on empirical allometric scaling equations (ASEs) that underpin all large-scale estimates of AGB. TLS provides extremely detailed non-destructive measurements of tree form independent of tree size and shape. We show examples of three-dimensional (3D) TLS measurements from various tropical and temperate forests and describe how the resulting TLS point clouds can be used to produce quantitative 3D models of branch and trunk size, shape and distribution. These models can drastically improve estimates of AGB, provide new, improved large-scale ASEs, and deliver insights into a range of fundamental tree properties related to structure. Large quantities of detailed measurements of individual 3D tree structure also have the potential to open new and exciting avenues of research in areas where difficulties of measurement have until now prevented statistical approaches to detecting and understanding underlying patterns of scaling, form and function. We discuss these opportunities and some of the challenges that remain to be overcome to enable wider adoption of TLS methods.

scholarly journals Structural 3D Reconstruction of Indoor Space for 5G Signal Simulation with Mobile Laser Scanning Point Clouds

2019 ◽  
Vol 11 (19) ◽  
pp. 2262 ◽  
Author(s):  
Cui ◽  
Li ◽  
Dong
Keyword(s):  
Indoor Environment ◽  
Laser Scanning ◽  
Large Scale ◽  
Point Clouds ◽  
Graph Cuts ◽  
3D Models ◽  
Base Station ◽  
Label Graph ◽  
Semantic Constraints ◽  
Signal Simulation

3D modelling of indoor environment is essential in smart city applications such as building information modelling (BIM), spatial location application, energy consumption estimation, and signal simulation, etc. Fast and stable reconstruction of 3D models from point clouds has already attracted considerable research interest. However, in the complex indoor environment, automated reconstruction of detailed 3D models still remains a serious challenge. To address these issues, this paper presents a novel method that couples linear structures with three-dimensional geometric surfaces to automatically reconstruct 3D models using point cloud data from mobile laser scanning. In our proposed approach, a fully automatic room segmentation is performed on the unstructured point clouds via multi-label graph cuts with semantic constraints, which can overcome the over-segmentation in the long corridor. Then, the horizontal slices of point clouds with individual room are projected onto the plane to form a binary image, which is followed by line extraction and regularization to generate floorplan lines. The 3D structured models are reconstructed by multi-label graph cuts, which is designed to combine segmented room, line and surface elements as semantic constraints. Finally, this paper proposed a novel application that 5G signal simulation based on the output structural model to aim at determining the optimal location of 5G small base station in a large-scale indoor scene for the future. Four datasets collected using handheld and backpack laser scanning systems in different locations were used to evaluate the proposed method. The results indicate our proposed methodology provides an accurate and efficient reconstruction of detailed structured models from complex indoor scenes.

scholarly journals GROUND AND AERIAL DIGITAL DOCUMENTATION OF CULTURAL HERITAGE: PROVIDING TOOLS FOR 3D EXPLOITATION OF ARCHAEOLOGICAL DATA

2017 ◽  
Vol XLII-2/W3 ◽  
pp. 141-145 ◽  
Author(s):  
G. Cantoro
Keyword(s):  
Laser Scanning ◽  
Large Scale ◽  
Point Clouds ◽  
3D Models ◽  
Aerial Photographs ◽  
Scientific Methods ◽  
Archaeological Data ◽  
3D Point Clouds ◽  
Digital Documentation ◽  
Time Changes

Archaeology is by its nature strictly connected with the physical landscape and as such it explores the inter-relations of individuals with places in which they leave and the nature that surrounds them. Since its earliest stages, archaeology demonstrated its permeability to scientific methods and innovative techniques or technologies. Archaeologists were indeed between the first to adopt GIS platforms (since already almost three decades) on large scale and are now between the most demanding customers for emerging technologies such as digital photogrammetry and drone-aided aerial photography. <br><br> This paper aims at presenting case studies where the “3D approach” can be critically analysed and compared with more traditional means of documentation. Spot-light is directed towards the benefits of a specifically designed platform for user to access the 3D point-clouds and explore their characteristics. Beside simple measuring and editing tools, models are presented in their actual context and location, with historical and archaeological information provided on the side. As final step of a parallel project on geo-referencing and making available a large archive of aerial photographs, 3D models derived from photogrammetric processing of images have been uploaded and linked to photo-footprints polygons. Of great importance in such context is the possibility to interchange the point-cloud colours with satellite imagery from OpenLayers. This approach makes it possible to explore different landscape configurations due to time-changes with simple clicks. <br><br> In these cases, photogrammetry or 3D laser scanning replaced, sided or integrated legacy documentation, creating at once a new set of information for forthcoming research and ideally new discoveries.

scholarly journals Using street view imagery for 3D survey of rock slope failures

2017 ◽  
Author(s):  
Jérémie Voumard ◽  
Antonio Abellan ◽  
Pierrick Nicolet ◽  
Marie-Aurélie Chanut ◽  
Marc-Henri Derron ◽  
...  
Keyword(s):  
3D Reconstruction ◽  
Large Scale ◽  
Rock Slope ◽  
Ground Truth ◽  
Point Clouds ◽  
3D Models ◽  
Time Frame ◽  
Slope Failures ◽  
The Road ◽  
Street View

Abstract. We discuss here the challenges and limitations on surveying rock slope failures using 3D reconstruction from images acquired from Street View Imagery (SVI) and processed with modern photogrammetric workflows. We show how the back in time function can be used for a 3D reconstruction of two or more image sets from the same site but at different instants of time, allowing for rock slope surveying. Three sites in the French alps were selected: (a) a cliff beside a road where a protective wall collapsed consisting on two images sets (60 and 50 images on each set) captured on a six years timeframe; (b) a large-scale active landslide located on a slope at 250 m from the road, using seven images sets (50 to 80 images per set) from five different time periods with three images sets for one period; (c) a cliff over a tunnel which has collapsed, using three images sets on a six years time-frame. The analysis includes the use of different commercially available Structure for Motion (SfM) programs and comparison between the so-extracted photogrammetric point clouds and a LiDAR derived mesh used as a ground truth. As a result, both landslide deformation together with estimation of fallen volumes were clearly identified in the point clouds. Results are site and software-dependent, as a function of the image set and number of images, with model accuracies ranging between 0.1 and 3.1 m in the best and worst scenario, respectively. Despite some clear limitations and challenges, this manuscript demonstrates that this original approach might allow obtaining preliminary 3D models of an area without on-field images. Furthermore, the pre-failure topography can be obtained for sites where it would not be available otherwise.

scholarly journals SYNERGISTIC EXPLOITATION OF GEOINFORMATION METHODS FOR POST-EARTHQUAKE 3D MAPPING OF VRISA TRADITIONAL SETTLEMENT, LESVOS ISLAND, GREECE

2018 ◽  
Vol XLII-3/W4 ◽  
pp. 491-498 ◽  
Author(s):  
N. Soulakellis ◽  
S. Chatzistamatis ◽  
C. Vasilakos ◽  
G. Tataris ◽  
A. Papakonstantinou ◽  
...  
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Laser Scanning ◽  
Large Scale ◽  
Point Clouds ◽  
3D Models ◽  
Unmanned Aircraft ◽  
Aerial Photographs ◽  
3D Mapping ◽  
The Impact

<p><strong>Abstract.</strong> The aim of this paper is to present the methodology followed and the results obtained by the synergistic exploitation of geo-information methods towards 3D mapping of the impact of the catastrophic earthquake of June 12th 2017 on the traditional settlement of Vrisa on the island of Lesvos, Greece. A campaign took place for collecting: a) more than 150 ground control points using an RTK system, b) more than 20.000 high-resolution terrestrial and aerial images using cameras and Unmanned Aircraft Systems and c) 140 point clouds by a 3D Terrestrial Laser Scanner. The Structure from Motion method has been applied on the high-resolution terrestrial and aerial photographs, for producing accurate and very detailed 3D models of the damaged buildings of the Vrisa settlement. Additionally, two Orthophoto maps and Digital Surface Models have been created, with a spatial resolution of 5&amp;thinsp;cm and 3&amp;thinsp;cm, respectively. The first orthophoto map has been created just one day after the earthquake, while the second one, a month later. In parallel, 3D laser scanning data have been exploited in order to validate the accuracy of the 3D models and the RTK measurements used for the geo-registration of all the above-mentioned datasets. The significant advantages of the proposed methodology are: a) the coverage of large scale areas; b) the production of 3D models having very high spatial resolution and c) the support of post-earthquake management and reconstruction processes of the Vrisa village, since such 3D information can serve all stakeholders, be it national and/or local organizations.</p>

scholarly journals Automated Visual Recognizability Evaluation of Traffic Sign Based on 3D LiDAR Point Clouds

2019 ◽  
Vol 11 (12) ◽  
pp. 1453 ◽  
Author(s):  
Shanxin Zhang ◽  
Cheng Wang ◽  
Lili Lin ◽  
Chenglu Wen ◽  
Chenhui Yang ◽  
...  
Keyword(s):  
Traffic Safety ◽  
Laser Scanning ◽  
Large Scale ◽  
Evaluation Method ◽  
Evaluation Model ◽  
Point Clouds ◽  
Traffic Sign ◽  
Traffic Signs ◽  
3D Point Clouds ◽  
Traffic Environment

Maintaining the high visual recognizability of traffic signs for traffic safety is a key matter for road network management. Mobile Laser Scanning (MLS) systems provide efficient way of 3D measurement over large-scale traffic environment. This paper presents a quantitative visual recognizability evaluation method for traffic signs in large-scale traffic environment based on traffic recognition theory and MLS 3D point clouds. We first propose the Visibility Evaluation Model (VEM) to quantitatively describe the visibility of traffic sign from any given viewpoint, then we proposed the concept of visual recognizability field and Traffic Sign Visual Recognizability Evaluation Model (TSVREM) to measure the visual recognizability of a traffic sign. Finally, we present an automatic TSVREM calculation algorithm for MLS 3D point clouds. Experimental results on real MLS 3D point clouds show that the proposed method is feasible and efficient.

scholarly journals TLS and SfM Approach for Bulk Density Determination of Excavated Heterogeneous Raw Materials

Minerals ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 174 ◽  
Author(s):  
Peter Blistan ◽  
Stanislav Jacko ◽  
Ľudovít Kovanič ◽  
Julián Kondela ◽  
Katarína Pukanská ◽  
...  
Keyword(s):  
Bulk Density ◽  
Laser Scanning ◽  
Large Scale ◽  
Raw Materials ◽  
Terrestrial Laser Scanning ◽  
Mineral Resources ◽  
Point Clouds ◽  
Raw Material

A frequently recurring problem in the extraction of mineral resources (especially heterogeneous mineral resources) is the rapid operative determination of the extracted quantity of raw material in a surface quarry. This paper deals with testing and analyzing the possibility of using unconventional methods such as digital close-range photogrammetry and terrestrial laser scanning in the process of determining the bulk density of raw material under in situ conditions. A model example of a heterogeneous deposit is the perlite deposit Lehôtka pod Brehmi (Slovakia). Classical laboratory methods for determining bulk density were used to verify the results of the in situ method of bulk density determination. Two large-scale samples (probes) with an approximate volume of 7 m3 and 9 m3 were realized in situ. 6 point samples (LITH) were taken for laboratory determination. By terrestrial laser scanning (TLS) measurement from 2 scanning stations, point clouds with approximately 163,000/143,000 points were obtained for each probe. For Structure-from-Motion (SfM) photogrammetry, 49/55 images were acquired for both probes, with final point clouds containing approximately 155,000/141,000 points. Subsequently, the bulk densities of the bulk samples were determined by the calculation from in situ measurements by TLS and SfM photogrammetry. Comparison of results of the field in situ measurements (1841 kg∙m−3) and laboratory measurements (1756 kg∙m−3) showed only a 4.5% difference in results between the two methods for determining the density of heterogeneous raw materials, confirming the accuracy of the used in situ methods. For the determination of the loosening coefficient, the material from both large-scale samples was transferred on a horizontal surface. Their volumes were determined by TLS. The loosening coefficient for the raw material of 1.38 was calculated from the resulting values.

scholarly journals DEM Extraction from ALS Point Clouds in Forest Areas via Graph Convolution Network

2020 ◽  
Vol 12 (1) ◽  
pp. 178 ◽  
Author(s):  
Jinming Zhang ◽  
Xiangyun Hu ◽  
Hengming Dai ◽  
ShenRun Qu
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Laser Scanning ◽  
Large Scale ◽  
Spatial Relationship ◽  
Point Clouds ◽  
Current Data ◽  
Data Sampling ◽  
Dynamic Graph ◽  
Convolution Model

It is difficult to extract a digital elevation model (DEM) from an airborne laser scanning (ALS) point cloud in a forest area because of the irregular and uneven distribution of ground and vegetation points. Machine learning, especially deep learning methods, has shown powerful feature extraction in accomplishing point cloud classification. However, most of the existing deep learning frameworks, such as PointNet, dynamic graph convolutional neural network (DGCNN), and SparseConvNet, cannot consider the particularity of ALS point clouds. For large-scene laser point clouds, the current data preprocessing methods are mostly based on random sampling, which is not suitable for DEM extraction tasks. In this study, we propose a novel data sampling algorithm for the data preparation of patch-based training and classification named T-Sampling. T-Sampling uses the set of the lowest points in a certain area as basic points with other points added to supplement it, which can guarantee the integrity of the terrain in the sampling area. In the learning part, we propose a new convolution model based on terrain named Tin-EdgeConv that fully considers the spatial relationship between ground and non-ground points when constructing a directed graph. We design a new network based on Tin-EdgeConv to extract local features and use PointNet architecture to extract global context information. Finally, we combine this information effectively with a designed attention fusion module. These aspects are important in achieving high classification accuracy. We evaluate the proposed method by using large-scale data from forest areas. Results show that our method is more accurate than existing algorithms.

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