Simplification method of 3D point cloud data for ray trace simulation in indoor environment

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.

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Segmentation of 3D Point Cloud Data Based on Supervoxel Technique

Procedia Computer Science ◽

10.1016/j.procs.2020.04.045 ◽

2020 ◽

Vol 171 ◽

pp. 427-435

Author(s):

R.S. Rampriya ◽

R. Suganya

Keyword(s):

Point Cloud ◽

3D Point Cloud ◽

Point Cloud Data ◽

Cloud Data

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

Volume 2: Additive Manufacturing; Materials ◽

10.1115/msec2017-2794 ◽

2017 ◽

Cited By ~ 3

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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Review of 3D Point Cloud Data Segmentation Methods

International Journal of Advanced Network Monitoring and Controls ◽

10.21307/ijanmc-2020-010 ◽

2020 ◽

Vol 5 (1) ◽

pp. 66-71

Author(s):

Xiaoyi Ruan ◽

Baolong Liu

Keyword(s):

Point Cloud ◽

3D Point Cloud ◽

Point Cloud Data ◽

Cloud Data ◽

Data Segmentation ◽

Segmentation Methods

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An Efficient Filtering Approach for Removing Outdoor Point Cloud Data of Manhattan-World Buildings

Remote Sensing ◽

10.3390/rs13193796 ◽

2021 ◽

Vol 13 (19) ◽

pp. 3796

Author(s):

Lei Fan ◽

Yuanzhi Cai

Keyword(s):

Point Cloud ◽

Indoor Environment ◽

Laser Scanning ◽

Point Cloud Data ◽

Hole Filling ◽

Cloud Data ◽

Indoor Space ◽

Wide Range ◽

Data Points ◽

Filtering Approach

Laser scanning is a popular means of acquiring the indoor scene data of buildings for a wide range of applications concerning indoor environment. During data acquisition, unwanted data points beyond the indoor space of interest can also be recorded due to the presence of openings, such as windows and doors on walls. For better visualization and further modeling, it is beneficial to filter out those data, which is often achieved manually in practice. To automate this process, an efficient image-based filtering approach was explored in this research. In this approach, a binary mask image was created and updated through mathematical morphology operations, hole filling and connectively analysis. The final mask obtained was used to remove the data points located outside the indoor space of interest. The application of the approach to several point cloud datasets considered confirms its ability to effectively keep the data points in the indoor space of interest with an average precision of 99.50%. The application cases also demonstrate the computational efficiency (0.53 s, at most) of the approach proposed.

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