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

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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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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BUILDING FEATURES RECOGNITION FROM UNCERTAIN 3D LIDAR POINT CLOUDS: A SEMANTIC APPROACH

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

10.5194/isprs-archives-xliii-b4-2020-89-2020 ◽

2020 ◽

Vol XLIII-B4-2020 ◽

pp. 89-96

Author(s):

X.-F. Xing ◽

M. A. Mostafavi

Keyword(s):

Point Cloud ◽

Evaluation Process ◽

Point Clouds ◽

Semantic Approach ◽

Semantic Reasoning ◽

Knowledge Based ◽

3D Point Clouds ◽

Building Components ◽

3D Lidar

Abstract. LiDAR technology allows rapid observation of high-resolution and precise 3D point clouds for diverse applications in urban and natural areas. However, uneven density and incomplete point clouds make LiDAR data processing more challenging for the extraction of semantic information on objects and their components. In this paper, we propose a knowledge based semantic reasoning solution for the recognition of building components (e.g. roofs) from segmentation results in the presence of uncertainties in LiDAR point clouds. The proposed solution uses a semantic reasoning approach as well as a similarity evaluation process for object recognition. We apply the proposed method to recognize buildings’ roof styles from a point cloud with uncertainty as a case study.

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Symmetry Analysis of Oriental Polygonal Pagodas Using 3D Point Clouds for Cultural Heritage

Sensors ◽

10.3390/s21041228 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1228

Author(s):

Ting On Chan ◽

Linyuan Xia ◽

Yimin Chen ◽

Wei Lang ◽

Tingting Chen ◽

...

Keyword(s):

Cultural Heritage ◽

Unmanned Aerial Vehicle ◽

Point Cloud ◽

Geometric Model ◽

Digital Camera ◽

Point Clouds ◽

Symmetry Analysis ◽

3D Point Clouds ◽

Transmission Towers ◽

Aerial Vehicle

Ancient pagodas are usually parts of hot tourist spots in many oriental countries due to their unique historical backgrounds. They are usually polygonal structures comprised by multiple floors, which are separated by eaves. In this paper, we propose a new method to investigate both the rotational and reflectional symmetry of such polygonal pagodas through developing novel geometric models to fit to the 3D point clouds obtained from photogrammetric reconstruction. The geometric model consists of multiple polygonal pyramid/prism models but has a common central axis. The method was verified by four datasets collected by an unmanned aerial vehicle (UAV) and a hand-held digital camera. The results indicate that the models fit accurately to the pagodas’ point clouds. The symmetry was realized by rotating and reflecting the pagodas’ point clouds after a complete leveling of the point cloud was achieved using the estimated central axes. The results show that there are RMSEs of 5.04 cm and 5.20 cm deviated from the perfect (theoretical) rotational and reflectional symmetries, respectively. This concludes that the examined pagodas are highly symmetric, both rotationally and reflectionally. The concept presented in the paper not only work for polygonal pagodas, but it can also be readily transformed and implemented for other applications for other pagoda-like objects such as transmission towers.

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Computational Geometry-Based Surface Reconstruction for Volume Estimation: A Case Study on Magnitude-Frequency Relations for a LiDAR-Derived Rockfall Inventory

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10030157 ◽

2021 ◽

Vol 10 (3) ◽

pp. 157

Author(s):

Paul-Mark DiFrancesco ◽

David A. Bonneau ◽

D. Jean Hutchinson

Keyword(s):

Computational Geometry ◽

Surface Reconstruction ◽

Power Law ◽

Point Cloud ◽

Point Clouds ◽

Volume Estimation ◽

Estimation Methods ◽

Small Scale ◽

Reconstruction Methods ◽

3D Volume

Key to the quantification of rockfall hazard is an understanding of its magnitude-frequency behaviour. Remote sensing has allowed for the accurate observation of rockfall activity, with methods being developed for digitally assembling the monitored occurrences into a rockfall database. A prevalent challenge is the quantification of rockfall volume, whilst fully considering the 3D information stored in each of the extracted rockfall point clouds. Surface reconstruction is utilized to construct a 3D digital surface representation, allowing for an estimation of the volume of space that a point cloud occupies. Given various point cloud imperfections, it is difficult for methods to generate digital surface representations of rockfall with detailed geometry and correct topology. In this study, we tested four different computational geometry-based surface reconstruction methods on a database comprised of 3668 rockfalls. The database was derived from a 5-year LiDAR monitoring campaign of an active rock slope in interior British Columbia, Canada. Each method resulted in a different magnitude-frequency distribution of rockfall. The implications of 3D volume estimation were demonstrated utilizing surface mesh visualization, cumulative magnitude-frequency plots, power-law fitting, and projected annual frequencies of rockfall occurrence. The 3D volume estimation methods caused a notable shift in the magnitude-frequency relations, while the power-law scaling parameters remained relatively similar. We determined that the optimal 3D volume calculation approach is a hybrid methodology comprised of the Power Crust reconstruction and the Alpha Solid reconstruction. The Alpha Solid approach is to be used on small-scale point clouds, characterized with high curvatures relative to their sampling density, which challenge the Power Crust sampling assumptions.

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Comparison of Depth Camera and Terrestrial Laser Scanner in Monitoring Structural Deflections

Sensors ◽

10.3390/s21010201 ◽

2020 ◽

Vol 21 (1) ◽

pp. 201

Author(s):

Michael Bekele Maru ◽

Donghwan Lee ◽

Kassahun Demissie Tola ◽

Seunghee Park

Keyword(s):

Optical Sensors ◽

Point Cloud ◽

Three Dimensional ◽

Laser Scanner ◽

Point Clouds ◽

Depth Camera ◽

Terrestrial Laser Scanner ◽

Cloud Data ◽

3D Point Clouds ◽

3D Information

Modeling a structure in the virtual world using three-dimensional (3D) information enhances our understanding, while also aiding in the visualization, of how a structure reacts to any disturbance. Generally, 3D point clouds are used for determining structural behavioral changes. Light detection and ranging (LiDAR) is one of the crucial ways by which a 3D point cloud dataset can be generated. Additionally, 3D cameras are commonly used to develop a point cloud containing many points on the external surface of an object around it. The main objective of this study was to compare the performance of optical sensors, namely a depth camera (DC) and terrestrial laser scanner (TLS) in estimating structural deflection. We also utilized bilateral filtering techniques, which are commonly used in image processing, on the point cloud data for enhancing their accuracy and increasing the application prospects of these sensors in structure health monitoring. The results from these sensors were validated by comparing them with the outputs from a linear variable differential transformer sensor, which was mounted on the beam during an indoor experiment. The results showed that the datasets obtained from both the sensors were acceptable for nominal deflections of 3 mm and above because the error range was less than ±10%. However, the result obtained from the TLS were better than those obtained from the DC.

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Object class segmentation of massive 3D point clouds of urban areas using point cloud topology

International Journal of Remote Sensing ◽

10.1080/01431161.2013.838710 ◽

2013 ◽

Vol 34 (23) ◽

pp. 8408-8424 ◽

Cited By ~ 20

Author(s):

Rico Richter ◽

Markus Behrens ◽

Jürgen Döllner

Keyword(s):

Urban Areas ◽

Point Cloud ◽

Point Clouds ◽

Object Class ◽

3D Point Clouds ◽

Object Class Segmentation ◽

Class Segmentation

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An Egg Volume Measurement System Based on the Microsoft Kinect

Sensors ◽

10.3390/s18082454 ◽

2018 ◽

Vol 18 (8) ◽

pp. 2454 ◽

Cited By ~ 11

Author(s):

Ting Chan ◽

Derek Lichti ◽

Adam Jahraus ◽

Hooman Esfandiari ◽

Herve Lahamy ◽

...

Keyword(s):

Least Squares ◽

Point Cloud ◽

Least Squares Method ◽

Point Clouds ◽

Volume Estimation ◽

Microsoft Kinect ◽

Estimation Accuracy ◽

Shape Parameters ◽

Post Processing ◽

Egg Volume

Measuring the volume of bird eggs is a very important task for the poultry industry and ornithological research due to the high revenue generated by the industry. In this paper, we describe a prototype of a new metrological system comprising a 3D range camera, Microsoft Kinect (Version 2) and a point cloud post-processing algorithm for the estimation of the egg volume. The system calculates the egg volume directly from the egg shape parameters estimated from the least-squares method in which the point clouds of eggs captured by the Kinect are fitted to novel geometric models of an egg in a 3D space. Using the models, the shape parameters of an egg are estimated along with the egg’s position and orientation simultaneously under the least-squares criterion. Four sets of experiments were performed to verify the functionality and the performance of the system, while volumes estimated from the conventional water displacement method and the point cloud captured by a survey-grade laser scanner serve as references. The results suggest that the method is straightforward, feasible and reliable with an average egg volume estimation accuracy 93.3% when compared to the reference volumes. As a prototype, the software part of the system was implemented in a post-processing mode. However, as the proposed processing techniques is computationally efficient, the prototype can be readily transformed into a real-time egg volume system.

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Food Volume Estimation Based on Deep Learning View Synthesis from a Single Depth Map

Nutrients ◽

10.3390/nu10122005 ◽

2018 ◽

Vol 10 (12) ◽

pp. 2005 ◽

Cited By ~ 12

Author(s):

Frank Lo ◽

Yingnan Sun ◽

Jianing Qiu ◽

Benny Lo

Keyword(s):

Neural Network ◽

Deep Learning ◽

Point Cloud ◽

Volume Estimation ◽

Assessment System ◽

View Synthesis ◽

Depth Image ◽

3D Point Cloud ◽

Viewing Angle ◽

3D Point Clouds

An objective dietary assessment system can help users to understand their dietary behavior and enable targeted interventions to address underlying health problems. To accurately quantify dietary intake, measurement of the portion size or food volume is required. For volume estimation, previous research studies mostly focused on using model-based or stereo-based approaches which rely on manual intervention or require users to capture multiple frames from different viewing angles which can be tedious. In this paper, a view synthesis approach based on deep learning is proposed to reconstruct 3D point clouds of food items and estimate the volume from a single depth image. A distinct neural network is designed to use a depth image from one viewing angle to predict another depth image captured from the corresponding opposite viewing angle. The whole 3D point cloud map is then reconstructed by fusing the initial data points with the synthesized points of the object items through the proposed point cloud completion and Iterative Closest Point (ICP) algorithms. Furthermore, a database with depth images of food object items captured from different viewing angles is constructed with image rendering and used to validate the proposed neural network. The methodology is then evaluated by comparing the volume estimated by the synthesized 3D point cloud with the ground truth volume of the object items.

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Fast and Robust Flight Altitude Estimation of Multirotor UAVs in Dynamic Unstructured Environments Using 3D Point Cloud Sensors

Aerospace ◽

10.3390/aerospace5030094 ◽

2018 ◽

Vol 5 (3) ◽

pp. 94 ◽

Cited By ~ 5

Author(s):

Hriday Bavle ◽

Jose Sanchez-Lopez ◽

Paloma Puente ◽

Alejandro Rodriguez-Ramos ◽

Carlos Sampedro ◽

...

Keyword(s):

Point Cloud ◽

Point Clouds ◽

Estimation Algorithm ◽

Indoor Environments ◽

3D Point Cloud ◽

Flight Altitude ◽

Cloud Data ◽

3D Point Clouds ◽

Laser Altimeters ◽

Altitude Estimation

This paper presents a fast and robust approach for estimating the flight altitude of multirotor Unmanned Aerial Vehicles (UAVs) using 3D point cloud sensors in cluttered, unstructured, and dynamic indoor environments. The objective is to present a flight altitude estimation algorithm, replacing the conventional sensors such as laser altimeters, barometers, or accelerometers, which have several limitations when used individually. Our proposed algorithm includes two stages: in the first stage, a fast clustering of the measured 3D point cloud data is performed, along with the segmentation of the clustered data into horizontal planes. In the second stage, these segmented horizontal planes are mapped based on the vertical distance with respect to the point cloud sensor frame of reference, in order to provide a robust flight altitude estimation even in presence of several static as well as dynamic ground obstacles. We validate our approach using the IROS 2011 Kinect dataset available in the literature, estimating the altitude of the RGB-D camera using the provided 3D point clouds. We further validate our approach using a point cloud sensor on board a UAV, by means of several autonomous real flights, closing its altitude control loop using the flight altitude estimated by our proposed method, in presence of several different static as well as dynamic ground obstacles. In addition, the implementation of our approach has been integrated in our open-source software framework for aerial robotics called Aerostack.

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