scholarly journals A human supervisory approach to modeling industrial scenes using geometric primitives

2002 ◽  
Author(s):  
J.P. Luck ◽  
C.Q. Little ◽  
R.S. Roberts
Keyword(s):  
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.

Inertial Aided 3D LiDAR SLAM with Hybrid Geometric Primitives in Large-scale Environments

2021 ◽  
Author(s):  
Wen Chen ◽  
Hongchao Zhao ◽  
Qi Shen ◽  
Chao Xiong ◽  
Shunbo Zhou ◽  
...  
Keyword(s):  
Large Scale ◽  
3D Lidar ◽  
Geometric Primitives

Finding Better Local Optima in Topology Optimization via Tunneling

2018 ◽  
Author(s):  
Shanglong Zhang ◽  
Julián A. Norato
Keyword(s):  
Topology Optimization ◽  
Optimization Problems ◽  
Structural Response ◽  
Free Form ◽  
Local Optima ◽  
Design Representation ◽  
Tunneling Method ◽  
Optimization Parameters ◽  
Gradient Based ◽  
Geometric Primitives

Topology optimization problems are typically non-convex, and as such, multiple local minima exist. Depending on the initial design, the type of optimization algorithm and the optimization parameters, gradient-based optimizers converge to one of those minima. Unfortunately, these minima can be highly suboptimal, particularly when the structural response is very non-linear or when multiple constraints are present. This issue is more pronounced in the topology optimization of geometric primitives, because the design representation is more compact and restricted than in free-form topology optimization. In this paper, we investigate the use of tunneling in topology optimization to move from a poor local minimum to a better one. The tunneling method used in this work is a gradient-based deterministic method that finds a better minimum than the previous one in a sequential manner. We demonstrate this approach via numerical examples and show that the coupling of the tunneling method with topology optimization leads to better designs.

Geometric Primitives

2011 ◽  
pp. 295-342 ◽  
Author(s):  
Fletcher Dunn ◽  
Ian Parberry
Keyword(s):  
Geometric Primitives

scholarly journals 3D Motion Trajectories Recognition using Optimal Set of Geometric Primitives, Angular and Statistical Features

2019 ◽  
Vol 9 (2) ◽  
pp. 2972-2979
Keyword(s):  
Cross Validation ◽  
Recognition Accuracy ◽  
Svm Classifier ◽  
Statistical Features ◽  
3D Motion ◽  
Motion Trajectories ◽  
Optimal Subset ◽  
Optimal Set ◽  
Geometric Primitives ◽  
Fold Cross Validation

This paper presents the 3D motion trajectories (lower case 3D alphabetic characters) recognition using optimal set of geometric primitives, angular and statistical features. It has been observed that the different combinations of these features have not been used in the literature for recognition of 3D characters. The standard dataset named “CHAR3D” has been used for analysis purpose. The dataset consists of 2858 character samples and each character sample is 3 dimensional pen tip velocity trajectory. In this dataset only single pen down segmented characters have been considered. The recognition has been performed using Random Forest (RF) and multiclass support vector machine (SVM) classifier on the optimal subset of extracted features. The best obtained recognition accuracy of 83.4% has been recorded using 3D points, angular and statistical features at 10 fold cross validation using SVM classifier. Moreover, the highest recognition accuracy of 96.88% has been recorded using an optimal subset of 32 dimensional features namely, geometric primitives, angular and statistical features at 10 fold cross validation by RF classifier.

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 Drawing Planar Graphs with Few Geometric Primitives

2018 ◽  
Vol 22 (2) ◽  
pp. 357-387 ◽  
Author(s):  
Gregor Hültenschmidt ◽  
Philipp Kindermann ◽  
Wouter Meulemans ◽  
André Schulz
Keyword(s):  
Planar Graphs ◽  
Geometric Primitives
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