scholarly journals Fit4CAD: A point cloud benchmark for fitting simple geometric primitives in CAD objects

2021 ◽  
Author(s):  
Chiara Romanengo ◽  
Andrea Raffo ◽  
Yifan Qie ◽  
Nabil Anwer ◽  
Bianca Falcidieno
Keyword(s):  
Point Cloud ◽  
Geometric Primitives

scholarly journals Matching Point Clouds with STL Models by Using the Principle Component Analysis and a Decomposition into Geometric Primitives

2021 ◽  
Vol 11 (5) ◽  
pp. 2268
Author(s):  
Erika Straková ◽  
Dalibor Lukáš ◽  
Zdenko Bobovský ◽  
Tomáš Kot ◽  
Milan Mihola ◽  
...  
Keyword(s):  
Point Cloud ◽  
Principal Component ◽  
Point Clouds ◽  
Component Analysis ◽  
Language Models ◽  
Computational Time ◽  
Dominant Eigenvalue ◽  
Eigenvalues And Eigenvectors ◽  
Correct Model ◽  
Geometric Primitives

While repairing industrial machines or vehicles, recognition of components is a critical and time-consuming task for a human. In this paper, we propose to automatize this task. We start with a Principal Component Analysis (PCA), which fits the scanned point cloud with an ellipsoid by computing the eigenvalues and eigenvectors of a 3-by-3 covariant matrix. In case there is a dominant eigenvalue, the point cloud is decomposed into two clusters to which the PCA is applied recursively. In case the matching is not unique, we continue to distinguish among several candidates. We decompose the point cloud into planar and cylindrical primitives and assign mutual features such as distance or angle to them. Finally, we refine the matching by comparing the matrices of mutual features of the primitives. This is a more computationally demanding but very robust method. We demonstrate the efficiency and robustness of the proposed methodology on a collection of 29 real scans and a database of 389 STL (Standard Triangle Language) models. As many as 27 scans are uniquely matched to their counterparts from the database, while in the remaining two cases, there is only one additional candidate besides the correct model. The overall computational time is about 10 min in MATLAB.

scholarly journals PROBABILISTIC FEASIBILITY OF THE RECONSTRUCTION PROCESS OF RUSSIAN-ORTHODOX CHURCHES

2016 ◽  
Vol XLI-B5 ◽  
pp. 221-227
Author(s):  
M. Chizhova ◽  
A. Brunn ◽  
U. Stilla
Keyword(s):  
Point Cloud ◽  
A Priori ◽  
Simulated Dataset ◽  
Russian Orthodox ◽  
3D Point Cloud ◽  
Probabilistic Framework ◽  
Reconstruction Process ◽  
Virtual Reconstruction ◽  
Cultural Constructions ◽  
Geometric Primitives

The cultural human heritage is important for the identity of following generations and has to be preserved in a suitable manner. In the course of time a lot of information about former cultural constructions has been lost because some objects were strongly damaged by natural erosion or on account of human work or were even destroyed. It is important to capture still available building parts of former buildings, mostly ruins. This data could be the basis for a virtual reconstruction. Laserscanning offers in principle the possibility to take up extensively surfaces of buildings in its actual status. <br><br> In this paper we assume a priori given 3d-laserscanner data, 3d point cloud for the partly destroyed church. There are many well known algorithms, that describe different methods of extraction and detection of geometric primitives, which are recognized separately in 3d points clouds. In our work we put them in a common probabilistic framework, which guides the complete reconstruction process of complex buildings, in our case russian-orthodox churches. <br><br> Churches are modeled with their functional volumetric components, enriched with a priori known probabilities, which are deduced from a database of russian-orthodox churches. Each set of components represents a complete church. The power of the new method is shown for a simulated dataset of 100 russian-orthodox churches.

scholarly journals TOWARDS A SEMANTIC INTERPRETATION OF URBAN AREAS WITH AIRBORNE SYNTHETIC APERTURE RADAR TOMOGRAPHY

2016 ◽  
Vol III-7 ◽  
pp. 235-242 ◽  
Author(s):  
O. D'Hondt ◽  
S. Guillaso ◽  
O. Hellwich
Keyword(s):  
Synthetic Aperture Radar ◽  
Urban Areas ◽  
Point Cloud ◽  
Semantic Interpretation ◽  
Synthetic Aperture ◽  
Classification Rules ◽  
Building Facades ◽  
The City ◽  
Geometric Primitives ◽  
Aperture Radar

In this paper, we introduce a method to detect and reconstruct building parts from tomographic Synthetic Aperture Radar (SAR) airborne data. Our approach extends recent works in two ways: first, the radiometric information is used to guide the extraction of geometric primitives. Second, building facades and roofs are extracted thanks to geometric classification rules. We demonstrate our method on a 3 image L-Band airborne dataset over the city of Dresden, Germany. Experiments show how our technique allows to use the complementarity between the radiometric image and the tomographic point cloud to extract buildings parts in challenging situations.

scholarly journals Out of Plumb Assessment for Cylindrical-Like Minaret Structures Using Geometric Primitives Fitting

2019 ◽  
Vol 8 (2) ◽  
pp. 64 ◽  
Author(s):  
Bashar Alsadik ◽  
Nagham Amer Abdulateef ◽  
Yousif Husain Khalaf
Keyword(s):  
Cultural Heritage ◽  
Point Cloud ◽  
Experimental Tests ◽  
Major Axis ◽  
Thin Body ◽  
Assessment Procedure ◽  
Axis Orientation ◽  
Heritage Sites ◽  
Dense Point ◽  
Geometric Primitives

Cultural heritage documentation and monitoring represents one of the major tasks for experts in the field of surveying, photogrammetry and geospatial engineering. Cultural heritage objects in countries like Iraq and Syria have suffered from intentional destruction or demolition during the last few years. Furthermore, many heritage sites in the mentioned places have an added religious value, and were either destroyed or are still in danger. Mosques, churches and shrines typically include one or multiple tower structures, and these towers or minarets are in many cases cylindrical-like objects. Because of their tall and relatively thin body, and adding in their age of construction, observing their inclination or out of plumb is of high importance. Accordingly, it is highly necessary for the continuous monitoring and assessment of their preservation and restoration. In this paper, we suggest an out of plumb assessment procedure using a geometric primitives least squares fitting technique, namely, cylinders, cones, and 3D circles. The approach is based on reconstructing a dense point cloud of the minaret tower which is scaled to reality by control points. Accordingly, the out of plumb is computed by fitting one of the mentioned 3D primitives to the minaret point cloud where its major axis orientation is computed. Two experimental tests of heritage objects in Iraq are presented: the lost heritage of the minaret al Hadbaa in the city of Mosul (1173 AD) and an existing inclined minaret of the religious shrine of Imam Musa AlKadhim in Baghdad (1058 AD). The results show the efficiency of the suggested methodology where the out of plumb is computed as 0.45m±1cm for the shrine minaret and 1.90m±10cm for the model of the minaret al Hadbaa.

scholarly journals DEM BASED REGISTRATION OF MULTI-SENSOR AIRBORNE POINT CLOUDS EXEMPLARY SHOWN ON A RIVER SIDE IN NON URBAN AREA

2018 ◽  
Vol XLII-2 ◽  
pp. 109-116 ◽  
Author(s):  
R. Boerner ◽  
Y. Xu ◽  
L. Hoegner ◽  
R. Baran ◽  
F. Steinbacher ◽  
...  
Keyword(s):  
Urban Areas ◽  
Point Cloud ◽  
Grid Cell ◽  
Point Clouds ◽  
Control Points ◽  
Building Structures ◽  
Optical Images ◽  
Key Points ◽  
Geometric Primitives ◽  
Center Cell

This paper presents a method to register photogrammetric point clouds generated from optical images acquired by UAV and aerial LIDAR point clouds. Normally, the registration of two airborne scans of the same scene is solved by the use of control points and the direct registration using GNSS and INS information. However, the registration of multi-sensor point clouds without control points is more complicated and challenging. For the scene of non urban areas, the registration task gets even more complicated, because it is hard to extract sufficient geometric primitives from the building structures. For our proposed method, an outdoor scene is tested providing almost no man-made objects. Therefore, it is nearly impossible to search for planar objects and use them for registration. With no geometric primitives extracted, the proposed method utilizes the structure of the 2.5D DEM created from the ground points of the point cloud. Besides, instead of using control points or key points, the method automatic detect key planes from the 2.5D DEM as correspondences. These key planes are detected on a regular grid by the use of a predefined mask. To mark a DEM grid cell as key plane the histogram of sums of the angles between the center cell is used. Afterwards, similarity values between two key planes are calculated based on the histogram differences and a RANSAC based strategy is adopted to find corresponding key planes and estimate the transformation parameters. Experiments conducted in this paper indicate that it is feasible to register multi sensor point clouds with a big difference in their ground sampling distances with respect to the used cell size of the 2.5D DEM.

scholarly journals PROBABILISTIC FEASIBILITY OF THE RECONSTRUCTION PROCESS OF RUSSIAN-ORTHODOX CHURCHES

2016 ◽  
Vol XLI-B5 ◽  
pp. 221-227
Author(s):  
M. Chizhova ◽  
A. Brunn ◽  
U. Stilla
Keyword(s):  
Point Cloud ◽  
A Priori ◽  
Simulated Dataset ◽  
Russian Orthodox ◽  
3D Point Cloud ◽  
Probabilistic Framework ◽  
Reconstruction Process ◽  
Virtual Reconstruction ◽  
Cultural Constructions ◽  
Geometric Primitives

The cultural human heritage is important for the identity of following generations and has to be preserved in a suitable manner. In the course of time a lot of information about former cultural constructions has been lost because some objects were strongly damaged by natural erosion or on account of human work or were even destroyed. It is important to capture still available building parts of former buildings, mostly ruins. This data could be the basis for a virtual reconstruction. Laserscanning offers in principle the possibility to take up extensively surfaces of buildings in its actual status. <br><br> In this paper we assume a priori given 3d-laserscanner data, 3d point cloud for the partly destroyed church. There are many well known algorithms, that describe different methods of extraction and detection of geometric primitives, which are recognized separately in 3d points clouds. In our work we put them in a common probabilistic framework, which guides the complete reconstruction process of complex buildings, in our case russian-orthodox churches. <br><br> Churches are modeled with their functional volumetric components, enriched with a priori known probabilities, which are deduced from a database of russian-orthodox churches. Each set of components represents a complete church. The power of the new method is shown for a simulated dataset of 100 russian-orthodox churches.

Robust Recovery of 3D Geometric Primitives From Point Cloud

2017 ◽  
Author(s):  
Xiang Yang ◽  
Peter Meer ◽  
Hae Chang Gea
Keyword(s):  
Point Cloud ◽  
Input Data ◽  
Inverse Design ◽  
Solid Model ◽  
Real Point ◽  
3D Point Cloud ◽  
Robust Method ◽  
Direct Modification ◽  
Fitting In ◽  
Geometric Primitives

A robust method for surface fitting in 3D point cloud is presented as an application of the robust estimation of multiple in-lier structures algorithm [1]. The geometric primitives such as planes, spheres and cylinders are detected from the point samples in the noisy dataset, without regenerating surface normals or mesh. The inlier points of different surfaces are classified and segmented, with the tolerance of error for each surface estimated adaptively from the input data. From the segmented points, designers can interact with the geometric primitives conveniently. Direct modification of 3D point cloud and inverse design of solid model can be applied. Both synthetic and real point cloud datasets are tested for the use of the robust algorithm.

Introducing data analytics to the robotic drilling process

2018 ◽  
Vol 45 (3) ◽  
pp. 371-378 ◽  
Author(s):  
Toufik Al Khawli ◽  
Hamza Bendemra ◽  
Muddasar Anwar ◽  
Dewald Swart ◽  
Jorge Dias
Keyword(s):  
Point Cloud ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Data Sets ◽  
Drilling Process ◽  
Data Set ◽  
Content Type ◽  
Robotic Drilling ◽  
Geometric Primitives ◽  
Three Dimensional Space

PurposeThis paper presents a method for extracting the geometric primitives of a circle in a three-dimensional space from a discrete point cloud data set obtained by a laser stripe sensor. This paper aims to first establish a reference frame for the robotic drilling process by detecting the position and orientation of a reference hole on structural parts in a pre-drilling step, and second, to perform quality inspection of the hole in a post-drilling step.Design/methodology/approachThe method is divided into the following steps: a plane is initially fitted on the data by evaluating the principle component analysis using singular value decomposition; the data points or measurements are then rotated around an arbitrary axis using the Rodrigues’ rotation formula such that the normal direction of the estimated plane and thez-axis direction is parallel; the Delaunay triangulation is constructed on the point cloud and the confidence interval is estimated for segmenting the data set located at the circular boundary; and finally, a circular profile is fitted on the extracted set and transformed back to the original position.FindingsThe geometric estimation of the circle in three-dimensional space constitutes of the position of the center, the diameter and the orientation, which is represented by the normal vector of the plane that the circle lives in. The method is applied on both simulated data set with the addition of several noise levels and experimental data sets. The main purpose of both the tests is to quantify the accuracy of the estimated diameter. The results show good accuracy (mean relative error < 1 per cent) and high robustness to noise.Research limitations/implicationsThe proposed method is applied here to estimate the geometric primitives of only one circle (the reference hole). If multiple circles are needed, an addition clustering procedure is required to cluster the segmented data into multiple data sets. Each data set represents a circle. Also, the method does not operate efficiently on a sparse data sets. Dense data are required to cover the hole (at least ten scans to cover the hole diameter).Practical implicationsResearchers and practitioners can integrate this method with several robotic manufacturing applications where high accuracy is required. The extracted position and orientation of the hole are used to minimize the positioning and alignment errors between the mounted tool tip and the workpiece.Originality/valueThe method introduces data analytics for estimating the geometric primitives in the robotic drilling application. The main advantage of the proposed method is to register the top surface of the workpiece with respect to robot base frame with a high accuracy. An accurate workpiece registration is extremely necessary in the lateral direction (identifying where to drill), as well as in the vertical direction (identifying how far to drill).

scholarly journals PHOTOGRAMMETRY AND COMPUTED TOMOGRAPHY POINT CLOUD REGISTRATION USING VIRTUAL CONTROL POINTS

2021 ◽  
Vol XLIII-B2-2021 ◽  
pp. 265-270
Author(s):  
K. Zhan ◽  
D. Fritsch ◽  
J. F. Wagner
Keyword(s):  
Computed Tomography ◽  
Point Cloud ◽  
Control Point ◽  
Point Clouds ◽  
Parameters Estimation ◽  
Control Points ◽  
Initial Alignment ◽  
3D Reconstructions ◽  
Virtual Control ◽  
Geometric Primitives

Abstract. In this paper we propose a virtual control point based method for the registration of photogrammetry and computed tomography (CT) data. Because of the fundamentally different two data sources, conventional registration methods, such as manual control points registration or 3D local feature-based registration, are not suitable. The registration objective of our application is about 3D reconstructions of gyroscopes, which contain abundant geometric primitives to be fitted in the point clouds. In the first place, photogrammetry and CT scanning are applied, respectively, for 3D reconstructions. Secondly, our workflow implements a segmentation after obtaining the surface point cloud from the complete CT volumetric data. Then geometric primitives are fitted in this point cloud benefitting from the less complex cluster segments. In the next step, intersection operations of the parametrized primitives generates virtual points, which are utilized as control points for the transformation parameters estimation. A random sample consensus (RANSAC) method is applied to find the correspondences of both virtual control point sets using corresponding descriptors and calculates the transformation matrix as an initial alignment for further refining the registration. The workflow is invariant to pose, resolution, completeness and noise within our validation process.

scholarly journals TOWARDS A SEMANTIC INTERPRETATION OF URBAN AREAS WITH AIRBORNE SYNTHETIC APERTURE RADAR TOMOGRAPHY

2016 ◽  
Vol III-7 ◽  
pp. 235-242
Author(s):  
O. D&apos;Hondt ◽  
S. Guillaso ◽  
O. Hellwich
Keyword(s):  
Synthetic Aperture Radar ◽  
Urban Areas ◽  
Point Cloud ◽  
Semantic Interpretation ◽  
Synthetic Aperture ◽  
Classification Rules ◽  
Building Facades ◽  
The City ◽  
Geometric Primitives ◽  
Aperture Radar

In this paper, we introduce a method to detect and reconstruct building parts from tomographic Synthetic Aperture Radar (SAR) airborne data. Our approach extends recent works in two ways: first, the radiometric information is used to guide the extraction of geometric primitives. Second, building facades and roofs are extracted thanks to geometric classification rules. We demonstrate our method on a 3 image L-Band airborne dataset over the city of Dresden, Germany. Experiments show how our technique allows to use the complementarity between the radiometric image and the tomographic point cloud to extract buildings parts in challenging situations.

Sign in / Sign up

Export Citation Format

Share Document