scholarly journals DIRECT CO-REGISTRATION OF TIR IMAGES AND MLS POINT CLOUDS BY CORRESPONDING KEYPOINTS

2019 ◽  
Vol IV-2/W7 ◽  
pp. 235-242
Author(s):  
J. Zhu ◽  
Y. Xu ◽  
L. Hoegner ◽  
U. Stilla
Keyword(s):  
Condition Monitoring ◽  
Point Cloud ◽  
Infrared Image ◽  
Point Clouds ◽  
Thermal Infrared ◽  
3D Models ◽  
Corner Detection ◽  
Leakage Detection ◽  
Registration Process ◽  
Camera Pose

<p><strong>Abstract.</strong> In this work, we discussed how to directly combine thermal infrared image (TIR) and the point cloud without additional assistance from GCPs or 3D models. Specifically, we propose a point-based co-registration process for combining the TIR image and the point cloud for the buildings. The keypoints are extracted from images and point clouds via primitive segmentation and corner detection, then pairs of corresponding points are identified manually. After that, the estimated camera pose can be computed with EPnP algorithm. Finally, the point cloud with thermal information provided by IR images can be generated as a result, which is helpful in the tasks such as energy inspection, leakage detection, and abnormal condition monitoring. This paper provides us more insight about the probability and ideas about the combining TIR image and point cloud.</p>

scholarly journals ANALYSIS OF 3D BUILDING MODELS ACCURACY BASED ON THE AIRBORNE LASER SCANNING POINT CLOUDS

2018 ◽  
Vol XLII-2 ◽  
pp. 797-804 ◽  
Author(s):  
W. Ostrowski ◽  
M. Pilarska ◽  
J. Charyton ◽  
K. Bakuła
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Large Scale ◽  
Point Clouds ◽  
3D Models ◽  
Airborne Laser Scanning ◽  
Aerial Images ◽  
Statistical Parameters ◽  
Airborne Laser ◽  
Building Models

Creating 3D building models in large scale is becoming more popular and finds many applications. Nowadays, a wide term “3D building models” can be applied to several types of products: well-known CityGML solid models (available on few Levels of Detail), which are mainly generated from Airborne Laser Scanning (ALS) data, as well as 3D mesh models that can be created from both nadir and oblique aerial images. City authorities and national mapping agencies are interested in obtaining the 3D building models. Apart from the completeness of the models, the accuracy aspect is also important. Final accuracy of a building model depends on various factors (accuracy of the source data, complexity of the roof shapes, etc.). In this paper the methodology of inspection of dataset containing 3D models is presented. The proposed approach check all building in dataset with comparison to ALS point clouds testing both: accuracy and level of details. Using analysis of statistical parameters for normal heights for reference point cloud and tested planes and segmentation of point cloud provides the tool that can indicate which building and which roof plane in do not fulfill requirement of model accuracy and detail correctness. Proposed method was tested on two datasets: solid and mesh model.

scholarly journals Accuracy assessment of building point clouds automatically generated from iphone images

2014 ◽  
Vol XL-5 ◽  
pp. 547-552 ◽  
Author(s):  
B. Sirmacek ◽  
R. Lindenbergh
Keyword(s):  
Measurement Errors ◽  
Point Cloud ◽  
Model Updating ◽  
Accuracy Assessment ◽  
Low Cost ◽  
Point Clouds ◽  
3D Models ◽  
Map Updating ◽  
Cloud Points ◽  
Point To Point

Low-cost sensor generated 3D models can be useful for quick 3D urban model updating, yet the quality of the models is questionable. In this article, we evaluate the reliability of an automatic point cloud generation method using multi-view iPhone images or an iPhone video file as an input. We register such automatically generated point cloud on a TLS point cloud of the same object to discuss accuracy, advantages and limitations of the iPhone generated point clouds. For the chosen example showcase, we have classified 1.23% of the iPhone point cloud points as outliers, and calculated the mean of the point to point distances to the TLS point cloud as 0.11 m. Since a TLS point cloud might also include measurement errors and noise, we computed local noise values for the point clouds from both sources. Mean (μ) and standard deviation (&amp;sigma;) of roughness histograms are calculated as (μ<sub>1</sub> = 0.44 m., &amp;sigma;<sub>1</sub> = 0.071 m.) and (μ<sub>2</sub> = 0.025 m., &amp;sigma;<sub>2</sub> = 0.037 m.) for the iPhone and TLS point clouds respectively. Our experimental results indicate possible usage of the proposed automatic 3D model generation framework for 3D urban map updating, fusion and detail enhancing, quick and real-time change detection purposes. However, further insights should be obtained first on the circumstances that are needed to guarantee a successful point cloud generation from smartphone images.

scholarly journals THERMAL 3D MODELS ENHANCEMENT BASED ON INTEGRATION WITH VISIBLE IMAGERY

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 263-269
Author(s):  
F. Dadras Javan ◽  
M. Savadkouhi
Keyword(s):  
High Resolution ◽  
Thermal Imaging ◽  
Point Cloud ◽  
Point Clouds ◽  
3D Models ◽  
Thermal Modelling ◽  
Surface Models ◽  
Geometric Quality ◽  
Imaging Sensors ◽  
Visible Imagery

Abstract. In the last few years, Unmanned Aerial Vehicles (UAVs) are being frequently used to acquire high resolution photogrammetric images and consequently producing Digital Surface Models (DSMs) and orthophotos in a photogrammetric procedure for topography and surface processing applications. Thermal imaging sensors are mostly used for interpretation and monitoring purposes because of lower geometric resolution. But yet, thermal mapping is getting more important in civil applications, as thermal sensors can be used in condition that visible sensors cannot, such as foggy weather and night times which is not possible for visible cameras. But, low geometric quality and resolution of thermal images is a main drawback that 3D thermal modelling are encountered with. This study aims to offer a solution for to fixing mentioned problem and generating a thermal 3D model with higher spatial resolution based on thermal and visible point clouds integration. This integration leads to generate a more accurate thermal point cloud and DEM with more density and resolution which is appropriate for 3D thermal modelling. The main steps of this study are: generating thermal and RGB point clouds separately, registration of them in two course and fine level and finally adding thermal information to RGB high resolution point cloud by interpolation concept. Experimental results are presented in a mesh that has more faces (With a factor of 23) which leads to a higher resolution textured mesh with thermal information.

scholarly journals A PROJECTION-BASED RECONSTRUCTION ALGORITHM FOR 3D MODELING OF BRIDGE STRUCTURES FROM DRONE-BASED POINT CLOUD

2021 ◽  
Vol XLVI-4/W1-2021 ◽  
pp. 77-83
Author(s):  
M. Mehranfar ◽  
H. Arefi ◽  
F. Alidoost
Keyword(s):  
Point Cloud ◽  
Clustering Algorithm ◽  
3D Model ◽  
Reconstruction Algorithm ◽  
Point Clouds ◽  
3D Models ◽  
Reconstruction Technique ◽  
Reconstruction Process ◽  
Median Error ◽  
Bridge Structures

Abstract. This paper presents a projection-based method for 3D bridge modeling using dense point clouds generated from drone-based images. The proposed workflow consists of hierarchical steps including point cloud segmentation, modeling of individual elements, and merging of individual models to generate the final 3D model. First, a fuzzy clustering algorithm including the height values and geometrical-spectral features is employed to segment the input point cloud into the main bridge elements. In the next step, a 2D projection-based reconstruction technique is developed to generate a 2D model for each element. Next, the 3D models are reconstructed by extruding the 2D models orthogonally to the projection plane. Finally, the reconstruction process is completed by merging individual 3D models and forming an integrated 3D model of the bridge structure in a CAD format. The results demonstrate the effectiveness of the proposed method to generate 3D models automatically with a median error of about 0.025 m between the elements’ dimensions in the reference and reconstructed models for two different bridge datasets.

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 PHOTOGRAMMETRIC SOLUTIONS FOR 3D MODELING OF CULTURAL HERITAGE SITES IN REMOTE AREAS

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 765-772
Author(s):  
A. Mostafavi ◽  
M. Scaioni ◽  
V. Yordanov
Keyword(s):  
Cultural Heritage ◽  
Point Cloud ◽  
Point Clouds ◽  
3D Models ◽  
Digital Cameras ◽  
Minimal Cost ◽  
Remote Areas ◽  
Heritage Sites ◽  
3D Point Clouds ◽  
Realistic Possibility

Abstract. The realistic possibility of using non-metric digital cameras to achieve reliable 3D models has eased the application of photogrammetry in different domains. Documentation, conservation and dissemination of the Cultural Heritage (CH) can be obtained and implemented through virtual copies and replicas. Structure-from-Motion (SfM) photogrammetry has widely proven its impressive potential for image-based 3D reconstruction resulting in great 3D point clouds’ acquisitions but at minimal cost. Images from Unmanned Aerial Vehicles (UAVs) can be also processed within SfM pipeline to obtain point cloud of Cultural Heritage sites in remote regions. Both aerial and terrestrial images can be integrated to obtain a more complete 3D. In this paper, the application of SfM photogrammetry for surveying of the Ziggurat Chogha Zanbil in Iran is presented. Here point clouds have been derived from oblique and nadir photos captured from UAV as well as terrestrial photos. The obtained four point clouds have been compared on the basis of different techniques to highlight differences among them.

scholarly journals VAULT MODELING WITH NEURAL NETWORKS

2019 ◽  
Vol XLII-2/W9 ◽  
pp. 81-86
Author(s):  
L. Barazzetti ◽  
M. Previtali
Keyword(s):  
Neural Networks ◽  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
3D Models ◽  
Information Modeling ◽  
Free Form ◽  
Mathematical Representation ◽  
Mathematical Functions ◽  
Digital Reconstruction

<p><strong>Abstract.</strong> Nowadays, the digital reconstruction of vaults is carried out using photogrammetric and laser scanning techniques able to capture the visible surface with dense point clouds. Then, different modeling strategies allow the generation of 3D models in various formats, such as meshes that interpolates the acquired point cloud, NURBS-based reconstructions based on manual, semi-automated, or automated procedures, and parametric objects for Building Information Modeling. This paper proposes a novel method that reconstructs the visible surface of a vault using neural networks. It is based on the assumption that vaults are not irregular free-form objects, but they can be reconstructed by mathematical functions calculated from the acquired point clouds. The proposed approach uses the point cloud to train a neural network that approximates vault surface. The achieved solution is not only able to consider the basic geometry of the vault, but also its irregularities that cannot be neglected in the case of accurate and detailed modeling projects of historical vaults. Considerations on the approximation capabilities of neural networks are illustrated and discussed along with the advantages of creating a mathematical representation encapsulated into a function.</p>

scholarly journals FUSION OF THERMAL AND VISIBLE POINT CLOUDS : APPLICATION TO THE VACHES NOIRES LANDSLIDE, NORMANDY, FRANCE

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 227-232
Author(s):  
V. Guilbert ◽  
R. Antoine ◽  
C. Heinkelé ◽  
O. Maquaire ◽  
S. Costa ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Thermal Model ◽  
Point Cloud ◽  
Point Clouds ◽  
Direct Analysis ◽  
Thermal Infrared ◽  
Geometric Configuration ◽  
Data Sets ◽  
Landslide Area ◽  
Aerial Vehicle

Abstract. In this paper, we present a methodology to fusion 3D visible and thermal infrared (TIR) information on a coastal landslide area located in Normandy, France. A reflex and TIR camera on-board an Unmanned Aerial Vehicle are utilized to generate a 3D visible and a thermal model using Photogrammetry. A Python-written algorithm is then used to associate the thermal scalar on the TIR model to the closest point on the visible point cloud, before applying α-blending to ease the visualization of both data sets. This methodology leads to the generation of an integrated 3D thermo-visible model, allowing the direct analysis of the surface temperatures, visible data and geometric configuration of the landslide.

scholarly journals Evaluation of Partially Overlapping 3D Point Cloud's Registration by using ICP variant and CloudCompare.

2014 ◽  
Vol XL-8 ◽  
pp. 891-897 ◽  
Author(s):  
Y. D. Rajendra ◽  
S. C. Mehrotra ◽  
K. V. Kale ◽  
R. R. Manza ◽  
R. K. Dhumal ◽  
...  
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Large Object ◽  
Point Cloud Registration ◽  
Icp Algorithm ◽  
Cloud Data ◽  
Registration Process ◽  
Heritage Site ◽  
Laser Scanners ◽  
Dense Point

Terrestrial Laser Scanners (TLS) are used to get dense point samples of large object’s surface. TLS is new and efficient method to digitize large object or scene. The collected point samples come into different formats and coordinates. Different scans are required to scan large object such as heritage site. Point cloud registration is considered as important task to bring different scans into whole 3D model in one coordinate system. Point clouds can be registered by using one of the three ways or combination of them, Target based, feature extraction, point cloud based. For the present study we have gone through Point Cloud Based registration approach. We have collected partially overlapped 3D Point Cloud data of Department of Computer Science & IT (DCSIT) building located in Dr. Babasaheb Ambedkar Marathwada University, Aurangabad. To get the complete point cloud information of the building we have taken 12 scans, 4 scans for exterior and 8 scans for interior façade data collection. There are various algorithms available in literature, but Iterative Closest Point (ICP) is most dominant algorithms. The various researchers have developed variants of ICP for better registration process. The ICP point cloud registration algorithm is based on the search of pairs of nearest points in a two adjacent scans and calculates the transformation parameters between them, it provides advantage that no artificial target is required for registration process. We studied and implemented three variants Brute Force, KDTree, Partial Matching of ICP algorithm in MATLAB. The result shows that the implemented version of ICP algorithm with its variants gives better result with speed and accuracy of registration as compared with CloudCompare Open Source software.

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