AMC Bridge, a vendor of choice for software development services in the areas of computer-aided design, engineering, manufacturing and construction, shares a result of research on self-organizing maps algorithms and their use for surface reconstruction.
Waltham, MA -- (ReleaseWire) -- 05/15/2018 -- Machine learning has been growing in popularity, and numerous industries working with large amounts of data have already recognized the value of this technology. AMC Bridge conducted a research on self-organizing maps algorithms and their use for surface reconstruction, which is an important trend in 3D scanning.
The aim of the research is to find the most suitable method based on Machine Learning unsupervised learning techniques for reconstruction of interior and exterior 3D scans of original objects. The problem is to recreate surfaces from a given point cloud within the shortest possible time and with a given quality criteria.
Results of the research are presented in an article, published on insideBIGDATA, a popular news outlet that distills news, strategies, products and services in the world of Big Data for data scientists as well as IT and business professionals. insideBIGDATA focuses on big data, data science, AI, machine learning, and deep learning.
In this article AMC Bridge analyzes and compares results obtained with the usage of two self-organizing map types.
The research demonstrates that Self-Organizing Maps are suitable for 3D Surface Reconstruction. To get more information on these algorithms performance and check visual examples, download the full article on insideBIGDATA.
About AMC Bridge
AMC Bridge is a vendor of choice for software development services in the areas of computer-aided design, engineering, manufacturing and construction. Since 1999, we have been delivering solutions for CAD, CAE, CAM, PDM, BIM and PLM applications. We have participated in the development of commercial software products and custom solutions for the engineering markets based on a variety of platforms, from desktop and web to mobile and cloud.
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