AUTOMATIC INTEGRATION OF LASER SCANNING AND PHOTOGRAMMETRIC POINT CLOUDS: FROM ACQUISITION TO CO-REGISTRATION

Abstract. Laser scanning systems have been developed to capture very high-resolution 3D point clouds and consequently acquire the object geometry. This object measuring technique has a high capacity for being utilized in a wide variety of applications such as indoor and outdoor modelling. The Terrestrial Laser Scanning (TLS) is used as an important data capturing measurement system to provide high quality point cloud from industrial or built-up environments. However, the static nature of the TLS and complexity of the industrial sites necessitate employing a complementary data capturing system e.g. cameras to fill the gaps in the TLS point cloud caused by occlusions which is very common in complex industrial areas. Moreover, employing images provide better radiometric and edge information. This motivated a joint project to develop a system for automatic and robust co-registration of TLS data and images directly, especially for complex objects. In this paper, the proposed methods for various components of this project including gap detection from point cloud, calculation of initial image capturing configuration, user interface and support system for the image capturing procedures, and co-registration between TLS point cloud and photogrammetric point cloud are presented. The primarily results on a complex industrial environment are promising.

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Photogrammetric Measurement of a Wooden Truss

Slovak Journal of Civil Engineering ◽

10.2478/sjce-2018-0022 ◽

2018 ◽

Vol 26 (4) ◽

pp. 1-10

Author(s):

Marián Marčiš ◽

Marek Fraštia

Keyword(s):

Point Cloud ◽

Laser Scanning ◽

Point Clouds ◽

3D Modelling ◽

Complex Objects ◽

Lighting Conditions ◽

3D Point Clouds ◽

Special Cases ◽

Specific Object ◽

Photogrammetric Measurement

Abstract Wooden trusses are a very specific object for measurement. They are often very complex and hard to reach; they are characterized by narrow spaces and low-lighting conditions. In recent years, laser scanning technology was mostly used for this task, because of its contactless nature, the possibility of measurement in the dark, and the robustness of the resulting 3D point clouds. Photogrammetry was mostly used in special cases, e.g., for the measurement of a few selected truss components, but not for the 3D modelling of an entire truss. However, the progress in computer vision algorithms is allowing us to accomplish image-based-modelling on very complex objects. The following contribution compares the point clouds of a wooden truss generated by the leading photogrammetry systems with a point cloud from laser scanning. The results confirm the interesting potential of actual photogrammetric methods in the modelling of complex objects such as wooden trusses.

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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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MULTI-SENSOR 3D RECORDING PIPELINE FOR THE DOCUMENTATION OF JAVANESE TEMPLES

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

10.5194/isprs-archives-xlii-2-w15-829-2019 ◽

2019 ◽

Vol XLII-2/W15 ◽

pp. 829-834

Author(s):

A. Murtiyos ◽

P. Grussenmeyer ◽

D. Suwardhi ◽

W. A. Fadilah ◽

H. A. Permana ◽

...

Keyword(s):

Point Cloud ◽

Laser Scanning ◽

Cloud Model ◽

Point Clouds ◽

Heritage Sites ◽

Advantages And Disadvantages ◽

3D Point Clouds ◽

Point Cloud Model ◽

Geometric Quality ◽

Different Characteristics

Abstract. 3D recording is an important procedure in the conservation of heritage sites. This past decade, a myriad of 3D sensors has appeared in the market with different advantages and disadvantages. Most notably, the laser scanning and photogrammetry methods have become some of the most used techniques in 3D recording. The integration of these different sensors is an interesting topic, one which will be discussed in this paper. Integration is an activity to combine two or more data with different characteristics to produce a 3D model with the best results. The discussion in this study includes the process of acquisition, processing, and analysis of the geometric quality from the results of the 3D recording process; starting with the acquisition method, registration and georeferencing process, up to the integration of laser scanning and photogrammetry 3D point clouds. The final result of the integration of the two point clouds is the 3D point cloud model that has become a single entity. Some detailed parts of the object of interest draw both geometric and textural information from photogrammetry, while laser scanning provided a point cloud depicting the overall overview of the building. The object used as our case study is Sari Temple, located in Special Region of Yogyakarta, Indonesia.

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Can 3D Point Clouds Replace GCPs?

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsannals-ii-5-347-2014 ◽

2014 ◽

Vol II-5 ◽

pp. 347-354 ◽

Cited By ~ 2

Author(s):

G. Stavropoulou ◽

G. Tzovla ◽

A. Georgopoulos

Keyword(s):

Point Cloud ◽

Laser Scanning ◽

Large Scale ◽

Point Clouds ◽

Field Work ◽

Surface Model ◽

Control Points ◽

3D Point Clouds ◽

Surface Models

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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Optimizing Wireless Sensor Network Installations by Visibility Analysis on 3D Point Clouds

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8100460 ◽

2019 ◽

Vol 8 (10) ◽

pp. 460

Author(s):

Gracchi ◽

Gigli ◽

Noël ◽

Jaboyedoff ◽

Madiai ◽

...

Keyword(s):

Wireless Sensor Network ◽

Sensor Network ◽

Point Cloud ◽

Laser Scanning ◽

Point Clouds ◽

Wireless Sensor ◽

3D Point Cloud ◽

Cloud Data ◽

3D Point Clouds ◽

Optimal Deployment

In this paper, a MATLAB tool for the automatic detection of the best locations to install a wireless sensor network (WSN) is presented. The implemented code works directly on high-resolution 3D point clouds and aims to help in positioning sensors that are part of a network requiring inter-visibility, namely, a clear line of sight (LOS). Indeed, with the development of LiDAR and Structure from Motion technologies, there is an opportunity to directly use 3D point cloud data to perform visibility analyses. By doing so, many disadvantages of traditional modelling and analysis methods can be bypassed. The algorithm points out the optimal deployment of devices following mainly two criteria: inter-visibility (using a modified version of the Hidden Point Removal operator) and inter-distance. Furthermore, an option to prioritize significant areas is provided. The proposed method was first validated on an artificial 3D model, and then on a landslide 3D point cloud acquired from terrestrial laser scanning for the real positioning of an ultrawide-band WSN already installed in 2016. The comparison between collected data and data acquired by the WSN installed following traditional patterns has demonstrated its ability for the optimal deployment of a WSN requiring inter-visibility.

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Evaluating the Quality of TLS Point Cloud Colorization

Remote Sensing ◽

10.3390/rs12172748 ◽

2020 ◽

Vol 12 (17) ◽

pp. 2748

Author(s):

Arttu Julin ◽

Matti Kurkela ◽

Toni Rantanen ◽

Juho-Pekka Virtanen ◽

Mikko Maksimainen ◽

...

Keyword(s):

Point Cloud ◽

Laser Scanning ◽

Production Efficiency ◽

Point Clouds ◽

Measuring Instruments ◽

Efficient Production ◽

White Balance ◽

3D Point Clouds ◽

Imaging Sensors

Terrestrial laser scanning (TLS) enables the efficient production of high-density colored 3D point clouds of real-world environments. An increasing number of applications from visual and automated interpretation to photorealistic 3D visualizations and experiences rely on accurate and reliable color information. However, insufficient attention has been put into evaluating the colorization quality of the 3D point clouds produced applying TLS. We have developed a method for the evaluation of the point cloud colorization quality of TLS systems with integrated imaging sensors. Our method assesses the capability of several tested systems to reproduce colors and details of a scene by measuring objective image quality metrics from 2D images that were rendered from 3D scanned test charts. The results suggest that the detected problems related to color reproduction (i.e., measured differences in color, white balance, and exposure) could be mitigated in data processing while the issues related to detail reproduction (i.e., measured sharpness and noise) are less in the control of a scanner user. Despite being commendable 3D measuring instruments, improving the colorization tools and workflows, and automated image processing pipelines would potentially increase not only the quality and production efficiency but also the applicability of colored 3D point clouds.

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INTERACTIVE CLASSIFICATION OF CONSTRUCTION MATERIALS: FEEDBACK DRIVEN FRAMEWORK FOR ANNOTATION AND ANALYSIS OF 3D POINT CLOUDS

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

10.5194/isprs-archives-xlii-2-w5-343-2017 ◽

2017 ◽

Vol XLII-2/W5 ◽

pp. 343-347

Author(s):

M. R. Hess ◽

V. Petrovic ◽

F. Kuester

Keyword(s):

Cultural Heritage ◽

Point Cloud ◽

Laser Scanning ◽

Building Materials ◽

Point Clouds ◽

Normal Vector ◽

Surface Geometry ◽

Historical Structures ◽

3D Point Clouds

Digital documentation of cultural heritage structures is increasingly more common through the application of different imaging techniques. Many works have focused on the application of laser scanning and photogrammetry techniques for the acquisition of threedimensional (3D) geometry detailing cultural heritage sites and structures. With an abundance of these 3D data assets, there must be a digital environment where these data can be visualized and analyzed. Presented here is a feedback driven visualization framework that seamlessly enables interactive exploration and manipulation of massive point cloud data. The focus of this work is on the classification of different building materials with the goal of building more accurate as-built information models of historical structures. User defined functions have been tested within the interactive point cloud visualization framework to evaluate automated and semi-automated classification of 3D point data. These functions include decisions based on observed color, laser intensity, normal vector or local surface geometry. Multiple case studies are presented here to demonstrate the flexibility and utility of the presented point cloud visualization framework to achieve classification objectives.

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Development of a Low-Cost System for 3D Orchard Mapping Integrating UGV and LiDAR

Plants ◽

10.3390/plants10122804 ◽

2021 ◽

Vol 10 (12) ◽

pp. 2804

Author(s):

Harold F. Murcia ◽

Sebastian Tilaguy ◽

Sofiane Ouazaa

Keyword(s):

Reference Values ◽

Point Cloud ◽

Laser Scanning ◽

Low Cost ◽

Point Clouds ◽

Individual Plant ◽

Individual Tree ◽

3D Point Clouds ◽

Mapping System

Growing evaluation in the early stages of crop development can be critical to eventual yield. Point clouds have been used for this purpose in tasks such as detection, characterization, phenotyping, and prediction on different crops with terrestrial mapping platforms based on laser scanning. 3D model generation requires the use of specialized measurement equipment, which limits access to this technology because of their complex and high cost, both hardware elements and data processing software. An unmanned 3D reconstruction mapping system of orchards or small crops has been developed to support the determination of morphological indices, allowing the individual calculation of the height and radius of the canopy of the trees to monitor plant growth. This paper presents the details on each development stage of a low-cost mapping system which integrates an Unmanned Ground Vehicle UGV and a 2D LiDAR to generate 3D point clouds. The sensing system for the data collection was developed from the design in mechanical, electronic, control, and software layers. The validation test was carried out on a citrus crop section by a comparison of distance and canopy height values obtained from our generated point cloud concerning the reference values obtained with a photogrammetry method. A 3D crop map was generated to provide a graphical view of the density of tree canopies in different sections which led to the determination of individual plant characteristics using a Python-assisted tool. Field evaluation results showed plant individual tree height and crown diameter with a root mean square error of around 30.8 and 45.7 cm between point cloud data and reference values.

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3D Point Clouds and Eye Tracking for Investigating the Perception and Acceptance of Power Lines in Different Landscapes

Multimodal Technologies and Interaction ◽

10.3390/mti3020040 ◽

2019 ◽

Vol 3 (2) ◽

pp. 40 ◽

Cited By ~ 1

Author(s):

Ulrike Wissen Hayek ◽

Kilian Müller ◽

Fabian Göbel ◽

Peter Kiefer ◽

Reto Spielhofer ◽

...

Keyword(s):

Eye Tracking ◽

Point Cloud ◽

Laser Scanning ◽

Point Clouds ◽

Power Lines ◽

Urban Landscapes ◽

3D Point Clouds ◽

Energy Turnaround ◽

Visual Landscape

The perception of the visual landscape impact is a significant factor explaining the public’s acceptance of energy infrastructure developments. Yet, there is lack of knowledge how people perceive and accept power lines in certain landscape types and in combination with wind turbines, a required setting to achieve goals of the energy turnaround. The goal of this work was to demonstrate how 3D point cloud visualizations could be used for an eye tracking study to systematically investigate the perception of landscape scenarios with power lines. 3D visualizations of near-natural and urban landscapes were prepared based on data from airborne and terrestrial laser scanning. These scenes were altered with varying amounts of the respective infrastructure, and they provided the stimuli in a laboratory experiment with 49 participants. Eye tracking and questionnaires served for measuring the participants’ responses. The results show that the point cloud-based simulations offered suitable stimuli for the eye tracking study. Particularly for the analysis of guided perceptions, the approach fostered an understanding of disturbing landscape elements. A comparative in situ eye tracking study is recommended to further evaluate the quality of the point cloud simulations, whether they produce similar responses as in the real world.

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BRUTE FORCE MATCHING BETWEEN CAMERA SHOTS AND SYNTHETIC IMAGES FROM POINT CLOUDS

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

10.5194/isprsarchives-xli-b5-771-2016 ◽

2016 ◽

Vol XLI-B5 ◽

pp. 771-777 ◽

Cited By ~ 1

Author(s):

R. Boerner ◽

M. Kröhnert

Keyword(s):

Point Cloud ◽

Laser Scanning ◽

Spatial Information ◽

Point Clouds ◽

Synthetic Image ◽

3D Point Cloud ◽

Point Cloud Data ◽

Cloud Data ◽

Force Matching ◽

3D Point Clouds

3D point clouds, acquired by state-of-the-art terrestrial laser scanning techniques (TLS), provide spatial information about accuracies up to several millimetres. Unfortunately, common TLS data has no spectral information about the covered scene. However, the matching of TLS data with images is important for monoplotting purposes and point cloud colouration. Well-established methods solve this issue by matching of close range images and point cloud data by fitting optical camera systems on top of laser scanners or rather using ground control points. The approach addressed in this paper aims for the matching of 2D image and 3D point cloud data from a freely moving camera within an environment covered by a large 3D point cloud, e.g. a 3D city model. The key advantage of the free movement affects augmented reality applications or real time measurements. Therefore, a so-called real image, captured by a smartphone camera, has to be matched with a so-called synthetic image which consists of reverse projected 3D point cloud data to a synthetic projection centre whose exterior orientation parameters match the parameters of the image, assuming an ideal distortion free camera.

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