A Comparison of Volumetric Reconstruction Methods of Archaeological Deposits Using Point-Cloud Data from Ahuahu, Aotearoa New Zealand

Collection of 3D data in archaeology is a long-standing practice. Traditionally, the focus of these data has been visualization as opposed to analysis. Three-dimensional data are often recorded during archaeological excavations, with the provenience of deposits, features, and artefacts documented by a variety of methods. Simple analysis of 3D data includes calculating the volumes of bound entities, such as deposits and features, and determining the spatial relationships of artifacts within these. The construction of these volumes presents challenges that originate in computer-aided design (CAD) but have implications for how data are used in archaeological analysis. We evaluate 3D construction processes using data from Waitetoke, Ahuahu Great Mercury Island, Aotearoa, New Zealand. Point clouds created with data collected by total station, photogrammetry, and terrestrial LiDAR using simultaneous localization and mapping (SLAM) are compared, as well as different methods for generating surface area and volumes with triangulated meshes and convex hulls. The differences between methods are evaluated and assessed in relation to analyzing artifact densities within deposits. While each method of 3D data acquisition and modeling has advantages in terms of accuracy and precision, other factors such as data collection and processing times must be considered when deciding on the most suitable.

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Integration of Constructive Solid Geometry and Boundary Representation (CSG-BRep) for 3D Modeling of Underground Cable Wells from Point Clouds

Remote Sensing ◽

10.3390/rs12091452 ◽

2020 ◽

Vol 12 (9) ◽

pp. 1452

Author(s):

Ming Huang ◽

Xueyu Wu ◽

Xianglei Liu ◽

Tianhang Meng ◽

Peiyuan Zhu

Keyword(s):

Point Cloud ◽

Three Dimensional ◽

Point Clouds ◽

Adaptive Method ◽

Boundary Representation ◽

Point Cloud Data ◽

Terrestrial Lidar ◽

Cloud Data ◽

Underground Cable ◽

3D Information

The preference of three-dimensional representation of underground cable wells from two-dimensional symbols is a developing trend, and three-dimensional (3D) point cloud data is widely used due to its high precision. In this study, we utilize the characteristics of 3D terrestrial lidar point cloud data to build a CSG-BRep 3D model of underground cable wells, whose spatial topological relationship is fully considered. In order to simplify the modeling process, first, point cloud simplification is performed; then, the point cloud main axis is extracted by OBB bounding box, and lastly the point cloud orientation correction is realized by quaternion rotation. Furthermore, employing the adaptive method, the top point cloud is extracted, and it is projected for boundary extraction. Thereupon, utilizing the boundary information, we design the 3D cable well model. Finally, the cable well component model is generated by scanning the original point cloud. The experiments demonstrate that, along with the algorithm being fast, the proposed model is effective at displaying the 3D information of the actual cable wells and meets the current production demands.

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Multi-Dimensional Underwater Point Cloud Detection Based on Deep Learning

Sensors ◽

10.3390/s21030884 ◽

2021 ◽

Vol 21 (3) ◽

pp. 884

Author(s):

Chia-Ming Tsai ◽

Yi-Horng Lai ◽

Yung-Da Sun ◽

Yu-Jen Chung ◽

Jau-Woei Perng

Keyword(s):

Deep Learning ◽

Point Cloud ◽

Three Dimensional ◽

Point Clouds ◽

Training Data ◽

Network Architectures ◽

Point Cloud Data ◽

Data Types ◽

Raw Data ◽

Cloud Data

Numerous sensors can obtain images or point cloud data on land, however, the rapid attenuation of electromagnetic signals and the lack of light in water have been observed to restrict sensing functions. This study expands the utilization of two- and three-dimensional detection technologies in underwater applications to detect abandoned tires. A three-dimensional acoustic sensor, the BV5000, is used in this study to collect underwater point cloud data. Some pre-processing steps are proposed to remove noise and the seabed from raw data. Point clouds are then processed to obtain two data types: a 2D image and a 3D point cloud. Deep learning methods with different dimensions are used to train the models. In the two-dimensional method, the point cloud is transferred into a bird’s eye view image. The Faster R-CNN and YOLOv3 network architectures are used to detect tires. Meanwhile, in the three-dimensional method, the point cloud associated with a tire is cut out from the raw data and is used as training data. The PointNet and PointConv network architectures are then used for tire classification. The results show that both approaches provide good accuracy.

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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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PARAMETRIC DESIGN OF DELTA ROBOT

CBU International Conference Proceedings ◽

10.12955/cbup.v4.856 ◽

2016 ◽

Vol 4 ◽

pp. 803-806 ◽

Cited By ~ 1

Author(s):

Mert Gürgen ◽

Cenk Eryılmaz ◽

Vasfi Emre Ömürlü

Keyword(s):

Computer Aided Design ◽

Parametric Design ◽

Three Dimensional ◽

Theoretical Data ◽

Technical Solution ◽

3D Cad ◽

3D Data ◽

Kinematic Behavior ◽

Delta Robot ◽

Aided Design

This article describes a sophisticated determination and presentation of a workspace volume for a delta robot, with consideration of its kinematic behavior. With the help of theoretical equations, optimization is performed with the aid of the stiffness and dexterity analysis. Theoretical substructure is coded in Matlab and three-dimensional (3D) data for delta robot are developed in computer-aided design (CAD) environment. In later stages of the project, both 3D and theoretical data are linked together and thus, with the changing design parameter of the robot itself, the Solidworks CAD output adapts and regenerates output with a new set of parameters. To achieve an optimum workspace volume with predefined parameters, a different set of robot parameters are iterated through design optimization in Matlab, and the delta robot design is finalized and illustrated in the 3D CAD environment, Solidworks. This study provides a technical solution to accomplish a generic delta robot with optimized workspace volume.

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Reconstruction of three-dimensional maps based on closed-form solutions of the variational problem of multisensor data registration

Доклады Академии наук ◽

10.31857/s0869-56524846672-677 ◽

2019 ◽

Vol 484 (6) ◽

pp. 672-677

Author(s):

A. V. Vokhmintcev ◽

A. V. Melnikov ◽

K. V. Mironov ◽

V. V. Burlutskiy

Keyword(s):

Closed Form ◽

Large Scale ◽

Three Dimensional ◽

Dynamic Environment ◽

Closed Form Solution ◽

Point Clouds ◽

Accurate Method ◽

Form Solution ◽

Closed Form Solutions ◽

Reconstruction Methods

A closed-form solution is proposed for the problem of minimizing a functional consisting of two terms measuring mean-square distances for visually associated characteristic points on an image and meansquare distances for point clouds in terms of a point-to-plane metric. An accurate method for reconstructing three-dimensional dynamic environment is presented, and the properties of closed-form solutions are described. The proposed approach improves the accuracy and convergence of reconstruction methods for complex and large-scale scenes.

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VISUALIZATION OF THE CONSTRUCTION OF ANCIENT ROMAN BUILDINGS IN OSTIA USING POINT CLOUD DATA

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

10.5194/isprs-archives-xlii-2-w3-345-2017 ◽

2017 ◽

Vol XLII-2/W3 ◽

pp. 345-352

Author(s):

Y. Hori ◽

T. Ogawa

Keyword(s):

Point Cloud ◽

Laser Scanning ◽

Feature Matching ◽

Three Dimensional ◽

Point Clouds ◽

Final Report ◽

Point Cloud Data ◽

Cloud Data ◽

Coverage Accuracy ◽

New Research

The implementation of laser scanning in the field of archaeology provides us with an entirely new dimension in research and surveying. It allows us to digitally recreate individual objects, or entire cities, using millions of three-dimensional points grouped together in what is referred to as "point clouds". In addition, the visualization of the point cloud data, which can be used in the final report by archaeologists and architects, should usually be produced as a JPG or TIFF file. Not only the visualization of point cloud data, but also re-examination of older data and new survey of the construction of Roman building applying remote-sensing technology for precise and detailed measurements afford new information that may lead to revising drawings of ancient buildings which had been adduced as evidence without any consideration of a degree of accuracy, and finally can provide new research of ancient buildings. We used laser scanners at fields because of its speed, comprehensive coverage, accuracy and flexibility of data manipulation. Therefore, we “skipped” many of post-processing and focused on the images created from the meta-data simply aligned using a tool which extended automatic feature-matching algorithm and a popular renderer that can provide graphic results.

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Living in the clouds: conceptual reconstructions of harbour structures

Journal of Cultural Heritage Management and Sustainable Development ◽

10.1108/jchmsd-08-2017-0052 ◽

2018 ◽

Vol 8 (4) ◽

pp. 405-419

Author(s):

Elizabeth Anne Shotton

Keyword(s):

Three Dimensional ◽

Point Clouds ◽

Heritage Management ◽

Historical Narrative ◽

Web Based ◽

Content Type ◽

Terrestrial Lidar ◽

Sea Levels ◽

Rising Sea Levels ◽

Scan Data

Purpose The harbours of Ireland, under threat from deterioration and rising sea levels, are being documented using terrestrial LiDAR augmented by archival research to develop comprehensive histories and timeline models for public dissemination. While methods to extract legible three-dimensional models from scan data have been developed and such operational formats for heritage management are imperative, the need for this format in interpretive visualisations should be reconsidered. The paper aims to discuss these issues. Design/methodology/approach Interpretive visualisations are forms of history making, where factual evidence is drawn together with conjecture to illustrate a plausible account of events, and differentiation between fact and conjecture is the key to their intellectual transparency. A procedure for superimposing conjectural reconstructions, generated using Rhinoceros and CloudCompare, on original scan data in Cyclone and visualised on a web-based viewer is discussed. Findings Embellishing scan data with conjectural elements to visualise the evolution of harbours is advantageous for both research and public dissemination. The accuracy and density of the scans enables the interrogation of the harbour form and the irregular details, the latter in danger of generalisation if translated into parametric or mesh format. Equally, the ethereal quality of the point cloud conveys a sense of tentativeness, consistent with a provisional hypothesis. Finally, coding conjectural elements allows users to intuit the difference between fact and historical narrative. Originality/value While various web-based point clouds viewers are used to disseminate research, the novelty here is the potential to develop didactic representations using point clouds that successfully capture a provisional thesis regarding each harbour’s evolution in an intellectually transparent manner to enable further inquiry.

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Recognition of Point Sets Objects in Realistic Scenes

Mobile Information Systems ◽

10.1155/2020/4925821 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Ruizhen Gao ◽

Xiaohui Li ◽

Jingjun Zhang

Keyword(s):

Network Structure ◽

Point Cloud ◽

Euclidean Distance ◽

Three Dimensional ◽

Point Clouds ◽

Accuracy Rate ◽

Cloud Data ◽

3D Scanners ◽

Deep Learning Network ◽

Realistic Scenes

With the emergence of new intelligent sensing technologies such as 3D scanners and stereo vision, high-quality point clouds have become very convenient and lower cost. The research of 3D object recognition based on point clouds has also received widespread attention. Point clouds are an important type of geometric data structure. Because of its irregular format, many researchers convert this data into regular three-dimensional voxel grids or image collections. However, this can lead to unnecessary bulk of data and cause problems. In this paper, we consider the problem of recognizing objects in realistic senses. We first use Euclidean distance clustering method to segment objects in realistic scenes. Then we use a deep learning network structure to directly extract features of the point cloud data to recognize the objects. Theoretically, this network structure shows strong performance. In experiment, there is an accuracy rate of 98.8% on the training set, and the accuracy rate in the experimental test set can reach 89.7%. The experimental results show that the network structure in this paper can accurately identify and classify point cloud objects in realistic scenes and maintain a certain accuracy when the number of point clouds is small, which is very robust.

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Toward a Novel Laser-Based Approach for Estimating Snow Interception

Remote Sensing ◽

10.3390/rs12071146 ◽

2020 ◽

Vol 12 (7) ◽

pp. 1146 ◽

Cited By ~ 1

Author(s):

Micah Russell ◽

Jan U. H. Eitel ◽

Andrew J. Maguire ◽

Timothy E. Link

Keyword(s):

Laser Scanning ◽

Three Dimensional ◽

Point Clouds ◽

Primary Objective ◽

Snow Accumulation ◽

Future Research ◽

Snow Density ◽

Canopy Interception ◽

Terrestrial Lidar ◽

Large Trees

Forests reduce snow accumulation on the ground through canopy interception and subsequent evaporative losses. To understand snow interception and associated hydrological processes, studies have typically relied on resource-intensive point scale measurements derived from weighed trees or indirect measurements that compared snow accumulation between forested sites and nearby clearings. Weighed trees are limited to small or medium-sized trees, and indirect comparisons can be confounded by wind redistribution of snow, branch unloading, and clearing size. A potential alternative method could use terrestrial lidar (light detection and ranging) because three-dimensional lidar point clouds can be generated for any size tree and can be utilized to calculate volume of the intercepted snow. The primary objective of this study was to provide a feasibility assessment for estimating snow interception volume with terrestrial laser scanning (TLS), providing information on challenges and opportunities for future research. During the winters of 2017 and 2018, intercepted snow masses were continuously measured for two model trees suspended from load-cells. Simultaneously, autonomous terrestrial lidar scanning (ATLS) was used to develop volumetric estimates of intercepted snow. Multiplying ATLS volume estimates by snow density estimates (derived from empirical models based on air temperature) enabled the comparison of predicted vs. measured snow mass. Results indicate agreement between predicted and measured values (R2 ≥ 0.69, RMSE ≥ 0.91 kg, slope ≥ 0.97, intercept ≥ −1.39) when multiplying TLS snow interception volume with a constant snow density estimate. These results suggest that TLS might be a viable alternative to traditional approaches for mapping snow interception, potentially useful for estimating snow loads on large trees, collecting data in difficult to access terrain, and calibrating snow interception models to new forest types around the globe.

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Kinect Depth Data Segmentation Based on Gauss Mixture Model Clustering

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.760-762.1556 ◽

2013 ◽

Vol 760-762 ◽

pp. 1556-1561

Author(s):

Ting Wei Du ◽

Bo Liu

Keyword(s):

Mixture Model ◽

Point Cloud ◽

Three Dimensional ◽

Image Data ◽

Point Clouds ◽

Gaussian Mixture ◽

Depth Image ◽

Cloud Data ◽

Mixture Model Clustering ◽

Gauss Mixture

Indoor scene understanding based on the depth image data is a cutting-edge issue in the field of three-dimensional computer vision. Taking the layout characteristics of the indoor scenes and more plane features in these scenes into account, this paper presents a depth image segmentation method based on Gauss Mixture Model clustering. First, transform the Kinect depth image data into point cloud which is in the form of discrete three-dimensional point data, and denoise and down-sample the point cloud data; second, calculate the point normal of all points in the entire point cloud, then cluster the entire normal using Gaussian Mixture Model, and finally implement the entire point clouds segmentation by RANSAC algorithm. Experimental results show that the divided regions have obvious boundaries and segmentation quality is above normal, and lay a good foundation for object recognition.

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