scholarly journals Curvature Adaptive 3D Scanning Transformation Calculation

2018 ◽  
Vol 62 (4) ◽  
pp. 107-116
Author(s):  
Adrián Mezei ◽  
Tibor Kovács
Keyword(s):  
Real Time ◽  
Point Cloud ◽  
Three Dimensional ◽  
Point Clouds ◽  
3D Scanning ◽  
Laser Line ◽  
3D Scanner ◽  
Three Dimensional Objects ◽  
Laser Scanners ◽  
Set Up

Three-dimensional objects can be scanned by 3D laser scanners that use active triangulation. These scanners create three-dimensional point clouds from the scanned objects. The laser line is identified in the images, which are captured at given transformations by the camera, and the point cloud can be calculated from these. The hardest challenge is to construct these transformations so that most of the surface can be captured. The result of a scanning may have missing parts because either not the best transformations were used or because some parts of the object cannot be scanned. Based on the results of the previous scans, a better transformation plan can be created, with which the next scan can be performed. In this paper, a method is proposed for transforming a special 3D scanner into a position from where the scanned point can be seen from an ideal angle. A method is described for estimating this transformation in real-time, so these can be calculated for every point of a previous scan to set up a next improved scan.

scholarly journals Technical Assessment of Historic Buildings on The Basis of Information Obtained from a Three-Dimensional Point Clouds / Ocena Stanu Technicznego Budynków Zabytkowych w Oparciu o Dane Uzyskane z Trójwymiarowej Chmury Punktów

2016 ◽  
Vol 20 (1) ◽  
pp. 71-78 ◽  
Author(s):  
Joanna A. Pawłowicz ◽  
Elżbieta Szafranko
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Point Clouds ◽  
Modern Method ◽  
Historic Buildings ◽  
High Complexity ◽  
3D Scanner ◽  
Laser Technology ◽  
Technical Assessment ◽  
Short Time

Abstract 3D scanning is the most modern method of unlimited possibilities based on laser technology. Its main advantage is the speed of obtaining large amounts of data in a very short time, which gives a huge advantage over existing methods of the measuring. Scanning provides opportunities for use in engineering works, geodetic and the inventory of buildings and objects of a high complexity, as well as in studies of damage or deformation of the structure. 3D scanner is a device, which with high accuracy collects data about the shape and texture of the tested object and its surroundings in the form of a point cloud.

The Advantages of Using Laser Scanners in Surveying in Protected Sites

2015 ◽  
pp. 382-402
Author(s):  
Gülhan Benli
Keyword(s):  
Protected Areas ◽  
Point Cloud ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Laser Scanners ◽  
Point Data

Since the 2000s, terrestrial laser scanning, as one of the methods used to document historical edifices in protected areas, has taken on greater importance because it mitigates the difficulties associated with working on large areas and saves time while also making it possible to better understand all the particularities of the area. Through this technology, comprehensive point data (point clouds) about the surface of an object can be generated in a highly accurate three-dimensional manner. Furthermore, with the proper software this three-dimensional point cloud data can be transformed into three-dimensional rendering/mapping/modeling and quantitative orthophotographs. In this chapter, the study will present the results of terrestrial laser scanning and surveying which was used to obtain three-dimensional point clouds through three-dimensional survey measurements and scans of silhouettes of streets in Fatih in Historic Peninsula in Istanbul, which were then transposed into survey images and drawings. The study will also cite examples of the facade mapping using terrestrial laser scanning data in Istanbul Historic Peninsula Project.

scholarly journals A Novel Preprocessing Method for Dynamic Point-Cloud Compression

2021 ◽  
Vol 11 (13) ◽  
pp. 5941
Author(s):  
Mun-yong Lee ◽  
Sang-ha Lee ◽  
Kye-dong Jung ◽  
Seung-hyun Lee ◽  
Soon-chul Kwon
Keyword(s):  
Data Processing ◽  
Real Time ◽  
Point Cloud ◽  
High Performance ◽  
Three Dimensional ◽  
Point Clouds ◽  
3D Models ◽  
File Size ◽  
3D Point Clouds ◽  
Computer Based

Computer-based data processing capabilities have evolved to handle a lot of information. As such, the complexity of three-dimensional (3D) models (e.g., animations or real-time voxels) containing large volumes of information has increased exponentially. This rapid increase in complexity has led to problems with recording and transmission. In this study, we propose a method of efficiently managing and compressing animation information stored in the 3D point-clouds sequence. A compressed point-cloud is created by reconfiguring the points based on their voxels. Compared with the original point-cloud, noise caused by errors is removed, and a preprocessing procedure that achieves high performance in a redundant processing algorithm is proposed. The results of experiments and rendering demonstrate an average file-size reduction of 40% using the proposed algorithm. Moreover, 13% of the over-lap data are extracted and removed, and the file size is further reduced.

scholarly journals Sloped Terrain Segmentation for Autonomous Drive Using Sparse 3D Point Cloud

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Seoungjae Cho ◽  
Jonghyun Kim ◽  
Warda Ikram ◽  
Kyungeun Cho ◽  
Young-Sik Jeong ◽  
...  
Keyword(s):  
Real Time ◽  
Point Cloud ◽  
Data Exchange ◽  
Three Dimensional ◽  
Point Clouds ◽  
Two Dimensions ◽  
3D Point Cloud ◽  
3D Point Clouds ◽  
Ubiquitous Environment ◽  
Ground Segmentation

A ubiquitous environment for road travel that uses wireless networks requires the minimization of data exchange between vehicles. An algorithm that can segment the ground in real time is necessary to obtain location data between vehicles simultaneously executing autonomous drive. This paper proposes a framework for segmenting the ground in real time using a sparse three-dimensional (3D) point cloud acquired from undulating terrain. A sparse 3D point cloud can be acquired by scanning the geography using light detection and ranging (LiDAR) sensors. For efficient ground segmentation, 3D point clouds are quantized in units of volume pixels (voxels) and overlapping data is eliminated. We reduce nonoverlapping voxels to two dimensions by implementing a lowermost heightmap. The ground area is determined on the basis of the number of voxels in each voxel group. We execute ground segmentation in real time by proposing an approach to minimize the comparison between neighboring voxels. Furthermore, we experimentally verify that ground segmentation can be executed at about 19.31 ms per frame.

scholarly journals Real-Time Plane Detection with Consistency from Point Cloud Sequences

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 140
Author(s):  
Jinxuan Xu ◽  
Qian Xie ◽  
Honghua Chen ◽  
Jun Wang
Keyword(s):  
Real Time ◽  
Point Cloud ◽  
Three Dimensional ◽  
Point Clouds ◽  
Detection Algorithm ◽  
Depth Sensors ◽  
3D Point Clouds ◽  
Detection Approach ◽  
Detection Stage ◽  
High Level

Real-time consistent plane detection (RCPD) from structured point cloud sequences facilitates various high-level computer vision and robotic tasks. However, it remains a challenge. Existing techniques for plane detection suffer from a long running time or the problem that the plane detection result is not precise. Meanwhile, labels of planes are not consistent over the whole image sequence due to plane loss in the detection stage. In order to resolve these issues, we propose a novel superpixel-based real-time plane detection approach, while keeping their consistencies over frames simultaneously. In summary, our method has the following key contributions: (i) a real-time plane detection algorithm to extract planes from raw structured three-dimensional (3D) point clouds collected by depth sensors; (ii) a superpixel-based segmentation method to make the detected plane exactly match its actual boundary; and, (iii) a robust strategy to recover the missing planes by utilizing the contextual correspondences information in adjacent frames. Extensive visual and numerical experiments demonstrate that our method outperforms state-of-the-art methods in terms of efficiency and accuracy.

scholarly journals Study on Real-Time Point Cloud Superimposition on Camera Image to Assist Environmental Three-Dimensional Laser Scanning

2021 ◽  
Vol 15 (3) ◽  
pp. 324-333
Author(s):  
Kenta Ohno ◽  
Hiroaki Date ◽  
Satoshi Kanai ◽  
◽  
Keyword(s):  
Real Time ◽  
Point Cloud ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Laser Scanner ◽  
Point Clouds ◽  
Laser Measurements ◽  
Attitude Error ◽  
Camera Image ◽  
Actual Environment

Recently, three-dimensional (3D) laser scanning technology using terrestrial laser scanner (TLS) has been widely used in the fields of plant manufacturing, civil engineering and construction, and surveying. It is desirable for the operator to be able to immediately and intuitively confirm the scanned point cloud to reduce unscanned regions and acquire scanned point clouds of high quality. Therefore, in this study, we developed a method to superimpose the point cloud on the actual environment to assist environmental 3D laser measurements, allowing the operator to check the scanned point cloud or unscanned regions in real time using the camera image. The method included extracting the correspondences of the camera image and the image generated by point clouds by considering unscanned regions, estimating the camera position and attitude in the point cloud by sampling correspondence points, and superimposing the scanned point cloud and unscanned regions on the camera image. When the proposed method was applied to two types of environments, that is, a boiler room and university office, the estimated camera image had a mean position error of approximately 150 mm and mean attitude error of approximately 1°, while the scanned point cloud and unscanned regions were superimposed on the camera image on a tablet PC at a rate of approximately 1 fps.

Handheld Laser Scanner Research

2019 ◽  
Vol 952 (10) ◽  
pp. 47-54
Author(s):  
A.V. Komissarov ◽  
A.V. Remizov ◽  
M.M. Shlyakhova ◽  
K.K. Yambaev
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Laser Scanner ◽  
Test Site ◽  
Optical Systems ◽  
Advantages And Disadvantages ◽  
Site Survey ◽  
Laser Scanners ◽  
Three Dimensional Models ◽  
The Stability

The authors consider hand-held laser scanners, as a new photogrammetric tool for obtaining three-dimensional models of objects. The principle of their work and the newest optical systems based on various sensors measuring the depth of space are described in detail. The method of simultaneous navigation and mapping (SLAM) used for combining single scans into point cloud is outlined. The formulated tasks and methods for performing studies of the DotProduct (USA) hand-held laser scanner DPI?8X based on a test site survey are presented. The accuracy requirements for determining the coordinates of polygon points are given. The essence of the performed experimental research of the DPI?8X scanner is described, including scanning of a test object at various scanner distances, shooting a test polygon from various scanner positions and building point cloud, repeatedly shooting the same area of the polygon to check the stability of the scanner. The data on the assessment of accuracy and analysis of research results are given. Fields of applying hand-held laser scanners, their advantages and disadvantages are identified.

scholarly journals Structured-light 3D scanning of exhibited historical clothing—a first-ever methodical trial and its results

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Jerzy Montusiewicz ◽  
Marek Miłosz ◽  
Jacek Kęsik ◽  
Kamil Żyła
Keyword(s):  
Cultural Heritage ◽  
Information Technologies ◽  
Structured Light ◽  
Three Dimensional ◽  
3D Models ◽  
3D Scanning ◽  
Dimensional Representation ◽  
3D Scanner ◽  
Three Dimensional Representation ◽  
3D Scanners

AbstractHistorical costumes are part of cultural heritage. Unlike architectural monuments, they are very fragile, which exacerbates the problems of their protection and popularisation. A big help in this can be the digitisation of their appearance, preferably using modern techniques of three-dimensional representation (3D). The article presents the results of the search for examples and methodologies of implementing 3D scanning of exhibited historical clothes as well as the attendant problems. From a review of scientific literature it turns out that so far practically no one in the world has made any methodical attempts at scanning historical clothes using structured-light 3D scanners (SLS) and developing an appropriate methodology. The vast majority of methods for creating 3D models of clothes used photogrammetry and 3D modelling software. Therefore, an innovative approach was proposed to the problem of creating 3D models of exhibited historical clothes through their digitalisation by means of a 3D scanner using structural light technology. A proposal for the methodology of this process and concrete examples of its implementation and results are presented. The problems related to the scanning of 3D historical clothes are also described, as well as a proposal how to solve them or minimise their impact. The implementation of the methodology is presented on the example of scanning elements of the Emir of Bukhara's costume (Uzbekistan) from the end of the nineteenth century, consisting of the gown, turban and shoes. Moreover, the way of using 3D models and information technologies to popularise cultural heritage in the space of digital resources is also discussed.

scholarly journals Multi-Dimensional Underwater Point Cloud Detection Based on Deep Learning

Sensors ◽  
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.

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

Sensors ◽  
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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