scholarly journals THE COMPARISON OF 2D AND 3D DETECTORS FOR TLS DATA REGISTRATION – PRELIMINARY RESULTS

2019 ◽  
Vol XLII-2/W9 ◽  
pp. 467-472 ◽  
Author(s):  
J. S. Markiewicz ◽  
Ł. Markiewicz ◽  
P. Foryś
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Scale Invariant ◽  
Data Registration ◽  
Registration Process ◽  
3D Data ◽  
Panoramic Images ◽  
Scale Invariant Feature ◽  
2D And 3D

<p><strong>Abstract.</strong> This paper presents the analysis of possible methods of a terrestrial laser scanning (TLS) data registration using 2D/3D detectors and descriptors. The developed approach, where point clouds are processed in form of panoramic images, orthoimages and 3D data, was described. The accuracy of the registration process was preliminary verified. The two approaches were analysed and compared: the 2D SIFT (Scale-Invariant Feature Transform) detector and descriptor with the rasterized TLS data and the 3D SIFT detector with the 3D FPFH (Fast Point Feature Histograms) descriptor. The feature points were found and preliminary matched using the OpenCV and PCL (Point Cloud Library) libraries. In order to find the best point cloud representation for the registration process, both the percentage and distribution of the correctly detected and matched points were analysed. The materials consisted of the point clouds of two chambers from the Museum of King John III’s Palace in Wilanów. They were acquired using the Z+F 5006h and 5003 TLS scanners. The performed analysis showed that the lowest RMSE values were for the 2D detectors and orthoimages. However, in the case of the point number and distribution, better results were obtained for using the 3D detector.</p>

scholarly journals Linear-Based Incremental Co-Registration of MLS and Photogrammetric Point Clouds

2021 ◽  
Vol 13 (11) ◽  
pp. 2195
Author(s):  
Shiming Li ◽  
Xuming Ge ◽  
Shengfu Li ◽  
Bo Xu ◽  
Zhendong Wang
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Registration Method ◽  
Cloud Data ◽  
Linear Feature ◽  
Data Registration ◽  
Source Point ◽  
Urban Remote Sensing ◽  
Single Data

Today, mobile laser scanning and oblique photogrammetry are two standard urban remote sensing acquisition methods, and the cross-source point-cloud data obtained using these methods have significant differences and complementarity. Accurate co-registration can make up for the limitations of a single data source, but many existing registration methods face critical challenges. Therefore, in this paper, we propose a systematic incremental registration method that can successfully register MLS and photogrammetric point clouds in the presence of a large number of missing data, large variations in point density, and scale differences. The robustness of this method is due to its elimination of noise in the extracted linear features and its 2D incremental registration strategy. There are three main contributions of our work: (1) the development of an end-to-end automatic cross-source point-cloud registration method; (2) a way to effectively extract the linear feature and restore the scale; and (3) an incremental registration strategy that simplifies the complex registration process. The experimental results show that this method can successfully achieve cross-source data registration, while other methods have difficulty obtaining satisfactory registration results efficiently. Moreover, this method can be extended to more point-cloud sources.

scholarly journals FINDING A GOOD FEATURE DETECTOR-DESCRIPTOR COMBINATION FOR THE 2D KEYPOINT-BASED REGISTRATION OF TLS POINT CLOUDS

2015 ◽  
Vol II-3/W5 ◽  
pp. 121-128 ◽  
Author(s):  
S. Urban ◽  
M. Weinmann
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Scale Invariant ◽  
Point Cloud Registration ◽  
Speeded Up Robust Features ◽  
Extensive Evaluation ◽  
Feature Correspondences ◽  
Intensity Images ◽  
Keypoint Detector

The automatic and accurate registration of terrestrial laser scanning (TLS) data is a topic of great interest in the domains of city modeling, construction surveying or cultural heritage. While numerous of the most recent approaches focus on keypoint-based point cloud registration relying on forward-projected 2D keypoints detected in panoramic intensity images, little attention has been paid to the selection of appropriate keypoint detector-descriptor combinations. Instead, keypoints are commonly detected and described by applying well-known methods such as the Scale Invariant Feature Transform (SIFT) or Speeded-Up Robust Features (SURF). In this paper, we present a framework for evaluating the influence of different keypoint detector-descriptor combinations on the results of point cloud registration. For this purpose, we involve five different approaches for extracting local features from the panoramic intensity images and exploit the range information of putative feature correspondences in order to define bearing vectors which, in turn, may be exploited to transfer the task of point cloud registration from the object space to the observation space. With an extensive evaluation of our framework on a standard benchmark TLS dataset, we clearly demonstrate that replacing SIFT and SURF detectors and descriptors by more recent approaches significantly alleviates point cloud registration in terms of accuracy, efficiency and robustness.

scholarly journals Architecture of Solution for Panoramic Image Blurring in GIS projects Application

2021 ◽  
Author(s):  
Dejan Vasić ◽  
Marina Davidović ◽  
Ivan Radosavljević ◽  
Đorđe Obradović
Keyword(s):  
Defect Detection ◽  
Point Cloud ◽  
Laser Scanning ◽  
Visual Inspection ◽  
Point Clouds ◽  
Data Sets ◽  
Data Set ◽  
Image Blurring ◽  
Panoramic Images ◽  
Spatial Entities

Abstract. Panoramic images captured using laser scanning technologies, which principally produce point clouds, are readily applicable in colorization of point cloud, detailed visual inspection, road defect detection, spatial entities extraction, diverse maps creation etc. This paper underlines the importance of images in modern surveying technologies and different GIS projects at the same time having regard to their anonymization in accordance with GDPR. Namely, it is a legislative requirement that faces of persons and license plates of vehicles in the collected data are blurred. The objective of this paper is to present a novel architecture of the solution for a particular object blurring. The methodology was tested on four data sets counting 5000, 10 000, 15 000 and 20 000 panoramic images respectively. Percentage of accuracy, i.e. successfully detected and blurred objects of interest, was higher than 97 % for each data set.

scholarly journals COMPARATIVE ANALYSIS OF DATA STRUCTURES FOR STORING MASSIVE TINS IN A DBMS

2016 ◽  
Vol XLI-B2 ◽  
pp. 123-130
Author(s):  
K. Kumar ◽  
H. Ledoux ◽  
J. Stoter
Keyword(s):  
Data Structures ◽  
Point Cloud ◽  
Laser Scanning ◽  
Compact Star ◽  
Point Clouds ◽  
Oracle Spatial ◽  
Cloud Data ◽  
3D Data ◽  
Edge Based ◽  
Elevation Model

Point cloud data are an important source for 3D geoinformation. Modern day 3D data acquisition and processing techniques such as airborne laser scanning and multi-beam echosounding generate billions of 3D points for simply an area of few square kilometers. With the size of the point clouds exceeding the billion mark for even a small area, there is a need for their efficient storage and management. These point clouds are sometimes associated with attributes and constraints as well. Storing billions of 3D points is currently possible which is confirmed by the initial implementations in Oracle Spatial SDO PC and the PostgreSQL Point Cloud extension. But to be able to analyse and extract useful information from point clouds, we need more than just points i.e. we require the surface defined by these points in space. There are different ways to represent surfaces in GIS including grids, TINs, boundary representations, etc. In this study, we investigate the database solutions for the storage and management of massive TINs. The classical (face and edge based) and compact (star based) data structures are discussed at length with reference to their structure, advantages and limitations in handling massive triangulations and are compared with the current solution of PostGIS Simple Feature. The main test dataset is the TIN generated from third national elevation model of the Netherlands (AHN3) with a point density of over 10 points/m<sup>2</sup>. PostgreSQL/PostGIS DBMS is used for storing the generated TIN. The data structures are tested with the generated TIN models to account for their geometry, topology, storage, indexing, and loading time in a database. Our study is useful in identifying what are the limitations of the existing data structures for storing massive TINs and what is required to optimise these structures for managing massive triangulations in a database.

scholarly journals Data-Driven Point Cloud Objects Completion

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1514 ◽  
Author(s):  
Yang Zhang ◽  
Zhen Liu ◽  
Xiang Li ◽  
Yu Zang
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Large Scale ◽  
Point Clouds ◽  
Data Driven ◽  
3D Perception ◽  
Single View ◽  
Decoder Architecture ◽  
Scanned Image ◽  
2D And 3D

With the development of the laser scanning technique, it is easier to obtain 3D large-scale scene rapidly. However, many scanned objects may suffer serious incompletion caused by the scanning angles or occlusion, which has severely impacted their future usage for the 3D perception and modeling, while traditional point cloud completion methods often fails to provide satisfactory results due to the large missing parts. In this paper, by utilising 2D single-view images to infer 3D structures, we propose a data-driven Point Cloud Completion Network ( P C C N e t ), which is an image-guided deep-learning-based object completion framework. With the input of incomplete point clouds and the corresponding scanned image, the network can acquire enough completion rules through an encoder-decoder architecture. Based on an attention-based 2D-3D fusion module, the network is able to integrate 2D and 3D features adaptively according to their information integrity. We also propose a projection loss as an additional supervisor to have a consistent spatial distribution from multi-view observations. To demonstrate the effectiveness, first, the proposed P C C N e t is compared to recent generative networks and has shown more powerful 3D reconstruction abilities. Then, P C C N e t is compared to a recent point cloud completion methods, which has demonstrate that the proposed P C C N e t is able to provide satisfied completion results for objects with large missing parts.

scholarly journals Targetless Coregistration of Terrestrial Laser Scanning Point Clouds Using a Multi Surrounding Scan Image-Based Technique

2020 ◽  
Vol 1 (1) ◽  
pp. 01-07
Author(s):  
Bashar Alsadik
Keyword(s):  
Image Matching ◽  
Point Cloud ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Experimental Tests ◽  
Point Clouds ◽  
Data Set ◽  
Panoramic Images ◽  
Fine Registration ◽  
Rgb Values

The coregistration of terrestrial laser point clouds is widely investigated where different techniques are presented to solve this problem. The techniques are divided either as target-based or targetless approaches for coarse and fine coregistration. The targetless approach is more challenging since no physical reference targets are placed in the field during the scanning. Mainly, targetless methods are image-based and they are applied through projecting the point clouds back to the scanning stations. The projected 360 point cloud images are normally in the form of panoramic images utilizing either intensity or RGB values, and an image matching is followed to align the scan stations together. However, the point cloud coregistration is still a challenge since ICP like methods are applicable for fine registration. Furthermore, image-based approaches are restricted when there is: a limited overlap between point clouds, no RGB data accompanied to intensity values, and unstructured scanned objects in the point clouds. Therefore, we present in this paper the concept of a multi surrounding scan MSS image-based approach to overcome the difficulty to register point clouds in challenging cases. The multi surrounding scan approach means to create multi-perspective images per laser scan point cloud. These multi-perspective images will offer different viewpoints per scan station to overcome the viewpoint distortion that causes the failure of the image matching in challenging situations. Two experimental tests are applied using point clouds collected in Enschede city and the published 3D toolkit data set in Bremen city. The experiments showed a successful coregistration approach even in challenging settings with different constellations.

scholarly journals THE EXAMPLE OF USING INTENSITY ORTHOIMAGES IN TLS DATA REGISTRATION &ndash; A CASE STUDY

2017 ◽  
Vol XLII-2/W3 ◽  
pp. 467-474 ◽  
Author(s):  
J. S. Markiewicz
Keyword(s):  
Laser Scanning ◽  
Laser Scanner ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Point Distribution ◽  
Data Registration ◽  
Depth Maps ◽  
Source Data ◽  
Panoramic Images

The paper presents the orientation analysis of terrestrial laser scanning (TLS) data. In the proposed data processing methodology, point clouds are considered as panoramic images and orthoimages enriched by depth maps. Computer vision (CV) algorithms were used for the orientation; they were applied to test the correctness of the detection of tie points, as well as the accuracy, number and point distribution. For the source data, point clouds acquired from the terrestrial laser scanner Z+F 50063h for the two chambers in the Museum of King John III’s Palace in Wilanów were utilized.

scholarly journals COMPARATIVE ANALYSIS OF DATA STRUCTURES FOR STORING MASSIVE TINS IN A DBMS

2016 ◽  
Vol XLI-B2 ◽  
pp. 123-130 ◽  
Author(s):  
K. Kumar ◽  
H. Ledoux ◽  
J. Stoter
Keyword(s):  
Data Structures ◽  
Point Cloud ◽  
Laser Scanning ◽  
Compact Star ◽  
Point Clouds ◽  
Oracle Spatial ◽  
Cloud Data ◽  
3D Data ◽  
Edge Based ◽  
Elevation Model

Point cloud data are an important source for 3D geoinformation. Modern day 3D data acquisition and processing techniques such as airborne laser scanning and multi-beam echosounding generate billions of 3D points for simply an area of few square kilometers. With the size of the point clouds exceeding the billion mark for even a small area, there is a need for their efficient storage and management. These point clouds are sometimes associated with attributes and constraints as well. Storing billions of 3D points is currently possible which is confirmed by the initial implementations in Oracle Spatial SDO PC and the PostgreSQL Point Cloud extension. But to be able to analyse and extract useful information from point clouds, we need more than just points i.e. we require the surface defined by these points in space. There are different ways to represent surfaces in GIS including grids, TINs, boundary representations, etc. In this study, we investigate the database solutions for the storage and management of massive TINs. The classical (face and edge based) and compact (star based) data structures are discussed at length with reference to their structure, advantages and limitations in handling massive triangulations and are compared with the current solution of PostGIS Simple Feature. The main test dataset is the TIN generated from third national elevation model of the Netherlands (AHN3) with a point density of over 10 points/m&lt;sup&gt;2&lt;/sup&gt;. PostgreSQL/PostGIS DBMS is used for storing the generated TIN. The data structures are tested with the generated TIN models to account for their geometry, topology, storage, indexing, and loading time in a database. Our study is useful in identifying what are the limitations of the existing data structures for storing massive TINs and what is required to optimise these structures for managing massive triangulations in a database.

scholarly journals Revealing Changes in the Stem Form and Volume Allocation in Diverse Boreal Forests Using Two-Date Terrestrial Laser Scanning

Forests ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 835
Author(s):  
Ville Luoma ◽  
Tuomas Yrttimaa ◽  
Ville Kankare ◽  
Ninni Saarinen ◽  
Jiri Pyörälä ◽  
...  
Keyword(s):  
Tree Growth ◽  
Point Cloud ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Stem Volume ◽  
Accurate Information ◽  
Stem Form ◽  
Monitoring Period ◽  
Significant Difference

Tree growth is a multidimensional process that is affected by several factors. There is a continuous demand for improved information on tree growth and the ecological traits controlling it. This study aims at providing new approaches to improve ecological understanding of tree growth by the means of terrestrial laser scanning (TLS). Changes in tree stem form and stem volume allocation were investigated during a five-year monitoring period. In total, a selection of attributes from 736 trees from 37 sample plots representing different forest structures were extracted from taper curves derived from two-date TLS point clouds. The results of this study showed the capability of point cloud-based methods in detecting changes in the stem form and volume allocation. In addition, the results showed a significant difference between different forest structures in how relative stem volume and logwood volume increased during the monitoring period. Along with contributing to providing more accurate information for monitoring purposes in general, the findings of this study showed the ability and many possibilities of point cloud-based method to characterize changes in living organisms in particular, which further promote the feasibility of using point clouds as an observation method also in ecological studies.

scholarly journals Comparing High Accuracy t-LiDAR and UAV-SfM Derived Point Clouds for Geomorphological Change Detection

2021 ◽  
Vol 10 (6) ◽  
pp. 367
Author(s):  
Simoni Alexiou ◽  
Georgios Deligiannakis ◽  
Aggelos Pallikarakis ◽  
Ioannis Papanikolaou ◽  
Emmanouil Psomiadis ◽  
...  
Keyword(s):  
Soil Erosion ◽  
Change Detection ◽  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Mean Value ◽  
Detection Methods ◽  
Mountainous Catchments ◽  
Aerial Vehicle ◽  
Soil Erosion Assessment

Analysis of two small semi-mountainous catchments in central Evia island, Greece, highlights the advantages of Unmanned Aerial Vehicle (UAV) and Terrestrial Laser Scanning (TLS) based change detection methods. We use point clouds derived by both methods in two sites (S1 & S2), to analyse the effects of a recent wildfire on soil erosion. Results indicate that topsoil’s movements in the order of a few centimetres, occurring within a few months, can be estimated. Erosion at S2 is precisely delineated by both methods, yielding a mean value of 1.5 cm within four months. At S1, UAV-derived point clouds’ comparison quantifies annual soil erosion more accurately, showing a maximum annual erosion rate of 48 cm. UAV-derived point clouds appear to be more accurate for channel erosion display and measurement, while the slope wash is more precisely estimated using TLS. Analysis of Point Cloud time series is a reliable and fast process for soil erosion assessment, especially in rapidly changing environments with difficult access for direct measurement methods. This study will contribute to proper georesource management by defining the best-suited methodology for soil erosion assessment after a wildfire in Mediterranean environments.

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