Article
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Environment Mapping Based Classification for Reverse Engineering Using Supervised Learning
Version 1
: Received: 20 August 2024 / Approved: 21 August 2024 / Online: 22 August 2024 (02:35:40 CEST)
How to cite: Lim, S. Environment Mapping Based Classification for Reverse Engineering Using Supervised Learning. Preprints 2024, 2024081525. https://doi.org/10.20944/preprints202408.1525.v1 Lim, S. Environment Mapping Based Classification for Reverse Engineering Using Supervised Learning. Preprints 2024, 2024081525. https://doi.org/10.20944/preprints202408.1525.v1
Abstract
With the widespread adoption of 3D scanners, reverse engineering methods for obtaining CAD drawings from physical objects have become increasingly utilized. When converting point data obtained from a 3D scanner into a mesh structure, a smoothing process is typically applied because the point data often contains a lot of noise. However, this can result in the loss of important high-frequency details, or in cases where significant high-frequency information remains, there are limitations in representing the object with basic geometric shapes. In this paper, a method to address this issue by using environment mapping and supervised learning techniques is proposed. By mapping the object to an environment and then using supervised learning to analyze the data, it can accurately identify the basic geometric shapes. This approach reduces the working time and allows for easier design of complex surfaces that were previously difficult to model.
Keywords
reverse engineering; classification; environment mapping; supervised learning
Subject
Engineering, Electrical and Electronic Engineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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