Can 3D Point Clouds Replace GCPs?

Over the past decade, large-scale photogrammetric products have been extensively used for the geometric documentation of cultural heritage monuments, as they combine metric information with the qualities of an image document. Additionally, the rising technology of terrestrial laser scanning has enabled the easier and faster production of accurate digital surface models (DSM), which have in turn contributed to the documentation of heavily textured monuments. However, due to the required accuracy of control points, the photogrammetric methods are always applied in combination with surveying measurements and hence are dependent on them. Along this line of thought, this paper explores the possibility of limiting the surveying measurements and the field work necessary for the production of large-scale photogrammetric products and proposes an alternative method on the basis of which the necessary control points instead of being measured with surveying procedures are chosen from a dense and accurate point cloud. Using this point cloud also as a surface model, the only field work necessary is the scanning of the object and image acquisition, which need not be subject to strict planning. To evaluate the proposed method an algorithm and the complementary interface were produced that allow the parallel manipulation of 3D point clouds and images and through which single image procedures take place. The paper concludes by presenting the results of a case study in the ancient temple of Hephaestus in Athens and by providing a set of guidelines for implementing effectively the method.

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IMPROVED TOPOGRAPHIC MODELS VIA CONCURRENT AIRBORNE LIDAR AND DENSE IMAGE MATCHING

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-2-w4-259-2017 ◽

2017 ◽

Vol IV-2/W4 ◽

pp. 259-266 ◽

Cited By ~ 7

Author(s):

G. Mandlburger ◽

K. Wenzel ◽

A. Spitzer ◽

N. Haala ◽

P. Glira ◽

...

Keyword(s):

Spatial Resolution ◽

Image Matching ◽

Point Cloud ◽

Laser Scanning ◽

Point Clouds ◽

Airborne Lidar ◽

Surface Model ◽

3D Point Clouds ◽

Dense Image ◽

Surface Models

Modern airborne sensors integrate laser scanners and digital cameras for capturing topographic data at high spatial resolution. The capability of penetrating vegetation through small openings in the foliage and the high ranging precision in the cm range have made airborne LiDAR the prime terrain acquisition technique. In the recent years dense image matching evolved rapidly and outperforms laser scanning meanwhile in terms of the achievable spatial resolution of the derived surface models. In our contribution we analyze the inherent properties and review the typical processing chains of both acquisition techniques. In addition, we present potential synergies of jointly processing image and laser data with emphasis on sensor orientation and point cloud fusion for digital surface model derivation. Test data were concurrently acquired with the RIEGL LMS-Q1560 sensor over the city of Melk, Austria, in January 2016 and served as basis for testing innovative processing strategies. We demonstrate that (i) systematic effects in the resulting scanned and matched 3D point clouds can be minimized based on a hybrid orientation procedure, (ii) systematic differences of the individual point clouds are observable at penetrable, vegetated surfaces due to the different measurement principles, and (iii) improved digital surface models can be derived combining the higher density of the matching point cloud and the higher reliability of LiDAR point clouds, especially in the narrow alleys and courtyards of the study site, a medieval city.

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COMPARISON OF UAV IMAGERY-DERIVED POINT CLOUD TO TERRESTRIAL LASER SCANNER POINT CLOUD

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-2-w5-149-2019 ◽

2019 ◽

Vol IV-2/W5 ◽

pp. 149-155 ◽

Cited By ~ 2

Author(s):

S. Peterson ◽

J. Lopez ◽

R. Munjy

Keyword(s):

Point Cloud ◽

Laser Scanning ◽

Focal Length ◽

Laser Scanner ◽

Digital Camera ◽

Point Clouds ◽

Surface Model ◽

Control Points ◽

Ground Control Points ◽

Aerial Vehicle

Abstract. A small unmanned aerial vehicle (UAV) with survey-grade GNSS positioning is used to produce a point cloud for topographic mapping and 3D reconstruction. The objective of this study is to assess the accuracy of a UAV imagery-derived point cloud by comparing a point cloud generated by terrestrial laser scanning (TLS). Imagery was collected over a 320&thinsp;m by 320&thinsp;m area with undulating terrain, containing 80 ground control points. A SenseFly eBee Plus fixed-wing platform with PPK positioning with a 10.6&thinsp;mm focal length and a 20&thinsp;MP digital camera was used to fly the area. Pix4Dmapper, a computer vision based commercial software, was used to process a photogrammetric block, constrained by 5 GCPs while obtaining cm-level RMSE based on the remaining 75 checkpoints. Based on results of automatic aerial triangulation, a point cloud and digital surface model (DSM) (2.5&thinsp;cm/pixel) are generated and their accuracy assessed. A bias less than 1 pixel was observed in elevations from the UAV DSM at the checkpoints. 31 registered TLS scans made up a point cloud of the same area with an observed horizontal root mean square error (RMSE) of 0.006m, and negligible vertical RMSE. Comparisons were made between fitted planes of extracted roof features of 2 buildings and centreline profile comparison of a road in both UAV and TLS point clouds. Comparisons showed an average +8&thinsp;cm bias with UAV point cloud computing too high in two features. No bias was observed in the roof features of the southernmost building.

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MATLAB Virtual Toolbox for Retrospective Rockfall Source Detection and Volume Estimation Using 3D Point Clouds: A Case Study of a Subalpine Molasse Cliff

Geosciences ◽

10.3390/geosciences11020075 ◽

2021 ◽

Vol 11 (2) ◽

pp. 75

Author(s):

Dario Carrea ◽

Antonio Abellan ◽

Marc-Henri Derron ◽

Neal Gauvin ◽

Michel Jaboyedoff

Keyword(s):

Point Cloud ◽

Natural Hazard ◽

Point Clouds ◽

Volume Estimation ◽

Natural Phenomena ◽

Frequency Distributions ◽

Erosion Rates ◽

3D Point Clouds

The use of 3D point clouds to improve the understanding of natural phenomena is currently applied in natural hazard investigations, including the quantification of rockfall activity. However, 3D point cloud treatment is typically accomplished using nondedicated (and not optimal) software. To fill this gap, we present an open-source, specific rockfall package in an object-oriented toolbox developed in the MATLAB® environment. The proposed package offers a complete and semiautomatic 3D solution that spans from extraction to identification and volume estimations of rockfall sources using state-of-the-art methods and newly implemented algorithms. To illustrate the capabilities of this package, we acquired a series of high-quality point clouds in a pilot study area referred to as the La Cornalle cliff (West Switzerland), obtained robust volume estimations at different volumetric scales, and derived rockfall magnitude–frequency distributions, which assisted in the assessment of rockfall activity and long-term erosion rates. An outcome of the case study shows the influence of the volume computation on the magnitude–frequency distribution and ensuing erosion process interpretation.

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

Remote Sensing ◽

10.3390/rs11121453 ◽

2019 ◽

Vol 11 (12) ◽

pp. 1453 ◽

Cited By ~ 5

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.

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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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Special Issue on “Terrestrial Laser Scanning”: Editors’ Notes

Sensors ◽

10.3390/s19204569 ◽

2019 ◽

Vol 19 (20) ◽

pp. 4569

Author(s):

Joan R. Rosell-Polo ◽

Eduard Gregorio ◽

Jordi Llorens

Keyword(s):

Point Cloud ◽

Laser Scanning ◽

Terrestrial Laser Scanning ◽

Point Clouds ◽

Special Issue ◽

Alternative Technologies ◽

Cloud Processing ◽

3D Point Clouds ◽

Point Cloud Processing

In this editorial, we provide an overview of the content of the special issue on “Terrestrial Laser Scanning”. The aim of this Special Issue is to bring together innovative developments and applications of terrestrial laser scanning (TLS), understood in a broad sense. Thus, although most contributions mainly involve the use of laser-based systems, other alternative technologies that also allow for obtaining 3D point clouds for the measurement and the 3D characterization of terrestrial targets, such as photogrammetry, are also considered. The 15 published contributions are mainly focused on the applications of TLS to the following three topics: TLS performance and point cloud processing, applications to civil engineering, and applications to plant characterization.

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

Remote Sensing ◽

10.3390/rs12010178 ◽

2020 ◽

Vol 12 (1) ◽

pp. 178 ◽

Cited By ~ 1

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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TUM-MLS-2016: An Annotated Mobile LiDAR Dataset of the TUM City Campus for Semantic Point Cloud Interpretation in Urban Areas

Remote Sensing ◽

10.3390/rs12111875 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1875 ◽

Cited By ~ 1

Author(s):

Jingwei Zhu ◽

Joachim Gehrung ◽

Rong Huang ◽

Björn Borgmann ◽

Zhenghao Sun ◽

...

Keyword(s):

Test Data ◽

Urban Areas ◽

Point Cloud ◽

Large Scale ◽

Point Clouds ◽

Semantic Interpretation ◽

3D Point Clouds ◽

Semantic Labeling ◽

Benchmark Datasets ◽

Semantic Point

In the past decade, a vast amount of strategies, methods, and algorithms have been developed to explore the semantic interpretation of 3D point clouds for extracting desirable information. To assess the performance of the developed algorithms or methods, public standard benchmark datasets should invariably be introduced and used, which serve as an indicator and ruler in the evaluation and comparison. In this work, we introduce and present large-scale Mobile LiDAR point clouds acquired at the city campus of the Technical University of Munich, which have been manually annotated and can be used for the evaluation of related algorithms and methods for semantic point cloud interpretation. We created three datasets from a measurement campaign conducted in April 2016, including a benchmark dataset for semantic labeling, test data for instance segmentation, and test data for annotated single 360 ° laser scans. These datasets cover an urban area of approximately 1 km long roadways and include more than 40 million annotated points with eight classes of objects labeled. Moreover, experiments were carried out with results from several baseline methods compared and analyzed, revealing the quality of this dataset and its effectiveness when using it for performance evaluation.

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Three-Dimensional Reconstruction of Structural Surface Model of Heritage Bridges Using UAV-Based Photogrammetric Point Clouds

Remote Sensing ◽

10.3390/rs11101204 ◽

2019 ◽

Vol 11 (10) ◽

pp. 1204 ◽

Cited By ~ 17

Author(s):

Yue Pan ◽

Yiqing Dong ◽

Dalei Wang ◽

Airong Chen ◽

Zhen Ye

Keyword(s):

Classification Tree ◽

Three Dimensional ◽

Point Clouds ◽

3D Models ◽

Surface Model ◽

Reconstruction Method ◽

3D Point Clouds ◽

Surface Models ◽

Structural Surface ◽

Better Than

Three-dimensional (3D) digital technology is essential to the maintenance and monitoring of cultural heritage sites. In the field of bridge engineering, 3D models generated from point clouds of existing bridges is drawing increasing attention. Currently, the widespread use of the unmanned aerial vehicle (UAV) provides a practical solution for generating 3D point clouds as well as models, which can drastically reduce the manual effort and cost involved. In this study, we present a semi-automated framework for generating structural surface models of heritage bridges. To be specific, we propose to tackle this challenge via a novel top-down method for segmenting main bridge components, combined with rule-based classification, to produce labeled 3D models from UAV photogrammetric point clouds. The point clouds of the heritage bridge are generated from the captured UAV images through the structure-from-motion workflow. A segmentation method is developed based on the supervoxel structure and global graph optimization, which can effectively separate bridge components based on geometric features. Then, recognition by the use of a classification tree and bridge geometry is utilized to recognize different structural elements from the obtained segments. Finally, surface modeling is conducted to generate surface models of the recognized elements. Experiments using two bridges in China demonstrate the potential of the presented structural model reconstruction method using UAV photogrammetry and point cloud processing in 3D digital documentation of heritage bridges. By using given markers, the reconstruction error of point clouds can be as small as 0.4%. Moreover, the precision and recall of segmentation results using testing date are better than 0.8, and a recognition accuracy better than 0.8 is achieved.

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Tunnel Deformation Inspection via Global Spatial Axis Extraction from 3D Raw Point Cloud

Sensors ◽

10.3390/s20236815 ◽

2020 ◽

Vol 20 (23) ◽

pp. 6815

Author(s):

Cheng Yi ◽

Dening Lu ◽

Qian Xie ◽

Jinxuan Xu ◽

Jun Wang

Keyword(s):

Cross Sections ◽

Point Cloud ◽

Large Scale ◽

Spline Approximation ◽

Point Clouds ◽

Driving Safety ◽

Inspection Method ◽

B Spline ◽

3D Point Clouds ◽

Spatial Axis

Global inspection of large-scale tunnels is a fundamental yet challenging task to ensure the structural stability of tunnels and driving safety. Advanced LiDAR scanners, which sample tunnels into 3D point clouds, are making their debut in the Tunnel Deformation Inspection (TDI). However, the acquired raw point clouds inevitably possess noticeable occlusions, missing areas, and noise/outliers. Considering the tunnel as a geometrical sweeping feature, we propose an effective tunnel deformation inspection algorithm by extracting the global spatial axis from the poor-quality raw point cloud. Essentially, we convert tunnel axis extraction into an iterative fitting optimization problem. Specifically, given the scanned raw point cloud of a tunnel, the initial design axis is sampled to generate a series of normal planes within the corresponding Frenet frame, followed by intersecting those planes with the tunnel point cloud to yield a sequence of cross sections. By fitting cross sections with circles, the fitted circle centers are approximated with a B-Spline curve, which is considered as an updated axis. The procedure of “circle fitting and B-SPline approximation” repeats iteratively until convergency, that is, the distance of each fitted circle center to the current axis is smaller than a given threshold. By this means, the spatial axis of the tunnel can be accurately obtained. Subsequently, according to the practical mechanism of tunnel deformation, we design a segmentation approach to partition cross sections into meaningful pieces, based on which various inspection parameters can be automatically computed regarding to tunnel deformation. A variety of practical experiments have demonstrated the feasibility and effectiveness of our inspection method.

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