Compare the feature quantities of existing shapes within the database! Use generative models such as VAE and GAN for training.
"Similarity judgment" refers to the process of searching for shapes similar to the input shape from a registered database. Feature quantities of the shape are extracted, and to compare them with the feature quantities of existing shapes in the database, generative models such as VAE and GAN are used for training. In many industries, such as manufacturing and construction, there is a shift towards design based on three-dimensional shapes using 3D CAD. Additionally, computer-aided technologies related to design and manufacturing, known as CAD, CAM, and CAE, are also based on three-dimensional shapes. 【Features】 - Extract feature quantities of shapes and compare them with existing shape feature quantities in the database. - Use generative models such as VAE and GAN for training. - Direct comparison using 3D shape recognition AI. - String comparison using text-based AI. *For more details, please refer to the PDF document or feel free to contact us.
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In the manufacturing industry, the use of three-dimensional data such as 3D CAD data is becoming widespread, but in AI, the use of two-dimensional data is mainstream, and the use of three-dimensional data is still not in sight. Our company is considering how to make better use of 3D CAD data in the manufacturing industry and contribute to improving QCD, and we are conducting research and development on three-dimensional shape recognition technology using AI and deep learning. Based on the latest AI research results, our company has successfully developed a three-dimensional AI model that can recognize three-dimensional shape data. With this groundbreaking three-dimensional AI technology, we will spread three-dimensional AI throughout the engineering chain centered on CAD, CAM, and CAE, contributing to the improvement of QCD. Moreover, three-dimensional AI is a new technology, and its potential is limitless. We will promote research and development of three-dimensional AI and spread this pioneering three-dimensional AI technology not only in the manufacturing industry but also in many other industries, contributing to the improvement of QCD.
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