scholarly journals Hough Transform Plane Detector

2011 ◽  
Author(s):  
Dorit Borrmann ◽  
David Doria
Keyword(s):  
Hough Transform ◽  
Point Cloud ◽  
3D Point Cloud ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Plane Detector

This document presents a VTK wrapper of an extracted portion of ‘3DTK - The 3D Toolkit’ (http://threedtk.de) to enable a developer to find planes in 3D point cloud data.

Fast Intent Prediction of Multi-Cyclists in 3D Point Cloud Data using Deep Neural Networks

2021 ◽  
Author(s):  
Khaled Saleh ◽  
Ahmed Abobakr ◽  
Mohammed Hossny ◽  
Darius Nahavandi ◽  
Julie Iskander ◽  
...  
Keyword(s):  
Neural Networks ◽  
Point Cloud ◽  
Deep Neural Networks ◽  
3D Point Cloud ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Intent Prediction

Continuous plane detection in point-cloud data based on 3D Hough Transform

2014 ◽  
Vol 25 (1) ◽  
pp. 86-97 ◽  
Author(s):  
Rostislav Hulik ◽  
Michal Spanel ◽  
Pavel Smrz ◽  
Zdenek Materna
Keyword(s):  
Hough Transform ◽  
Point Cloud ◽  
Point Cloud Data ◽  
Cloud Data ◽  
3D Hough Transform

Automated Surface Inspection Using 3D Point Cloud Data in Manufacturing: A Case Study

2018 ◽  
Author(s):  
Romina Dastoorian ◽  
Ahmad E. Elhabashy ◽  
Wenmeng Tian ◽  
Lee J. Wells ◽  
Jaime A. Camelio
Keyword(s):  
Point Cloud ◽  
Surface Defect ◽  
Fault Location ◽  
Three Dimensional ◽  
Practical Case ◽  
3D Point Cloud ◽  
Point Cloud Data ◽  
Defect Inspection ◽  
Cloud Data

With the latest advancements in three-dimensional (3D) measurement technologies, obtaining 3D point cloud data for inspection purposes in manufacturing is becoming more common. While 3D point cloud data allows for better inspection capabilities, their analysis is typically challenging. Especially with unstructured 3D point cloud data, containing coordinates at random locations, the challenges increase with higher levels of noise and larger volumes of data. Hence, the objective of this paper is to extend the previously developed Adaptive Generalized Likelihood Ratio (AGLR) approach to handle unstructured 3D point cloud data used for automated surface defect inspection in manufacturing. More specifically, the AGLR approach was implemented in a practical case study to inspect twenty-seven samples, each with a unique fault. These faults were designed to cover an array of possible faults having three different sizes, three different magnitudes, and located in three different locations. The results show that the AGLR approach can indeed differentiate between non-faulty and a varying range of faulty surfaces while being able to pinpoint the fault location. This work also serves as a validation for the previously developed AGLR approach in a practical scenario.

Assessing the Geometric Integrity of Additive Manufactured Parts From Point Cloud Data Using Spectral Graph Theoretic Sparse Representation-Based Classification

2017 ◽  
Author(s):  
M. Samie Tootooni ◽  
Ashley Dsouza ◽  
Ryan Donovan ◽  
Prahalad K. Rao ◽  
Zhenyu (James) Kong ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Point Cloud ◽  
Extrusion Temperature ◽  
3D Point Cloud ◽  
Point Cloud Data ◽  
Modeling Framework ◽  
Cloud Data ◽  
Integrity Assessment ◽  
Graph Theoretic ◽  
Spectral Graph

This work proposes a novel approach for geometric integrity assessment of additive manufactured (AM, 3D printed) components, exemplified by acrylonitrile butadiene styrene (ABS) polymer parts made using fused filament fabrication (FFF) process. The following two research questions are addressed in this paper: (1) what is the effect of FFF process parameters, specifically, infill percentage (If) and extrusion temperature (Te) on geometric integrity of ABS parts?; and (2) what approach is required to differentiate AM parts with respect to their geometric integrity based on sparse sampling from a large (∼ 2 million data points) laser-scanned point cloud dataset? To answer the first question, ABS parts are produced by varying two FFF parameters, namely, infill percentage (If) and extrusion temperature (Te) through design of experiments. The part geometric integrity is assessed with respect to key geometric dimensioning and tolerancing (GD&T) features, such as flatness, circularity, cylindricity, root mean square deviation, and in-tolerance percentage. These GD&T parameters are obtained by laser scanning of the FFF parts. Concurrently, coordinate measurements of the part geometry in the form of 3D point cloud data is also acquired. Through response surface statistical analysis of this experimental data it was found that discrimination of geometric integrity between FFF parts based on GD&T parameters and process inputs alone was unsatisfactory (regression R2 < 50%). This directly motivates the second question. Accordingly, a data-driven analytical approach is proposed to classify the geometric integrity of FFF parts using minimal number (< 2% of total) of laser-scanned 3D point cloud data. The approach uses spectral graph theoretic Laplacian eigenvalues extracted from the 3D point cloud data in conjunction with a modeling framework called sparse representation to classify FFF part quality contingent on the geometric integrity. The practical outcome of this work is a method that can quickly classify the part geometric integrity with minimal point cloud data and high classification fidelity (F-score > 95%), which bypasses tedious coordinate measurement.

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