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A Study of 3D model voxelization method for artificial intelligence learning

Byongkwon Lee

Abstract


The 3D reconstruction technology which is one of various restoration technologies, implements and shapes 2D pixels of an object that actually exists in a 3D form. As an accurate 3D model for artificial intelligence learning information pre-processing is required. The pre-processing needs to accurately size information and coordinate information of a 3D object for artificial intelligence learning. Also, the 3D model data generated during the preprocessing can be represented as a point cloud, which is point-based coordinate data. In this study, the pixel data was analyzed by voxelizing the 3D model for artificial intelligence learning, and the 3D model data was digitized to form a mesh file. In this study, 3D modeling was done with a 3D modeling tool and the object was exported to STL. In addition, it was converted into mesh file data including 3D coordinate data in Python language on Google’s colab platform. The mesh data created in this way is used in DataSet for artificial intelligence learning (CNN, RNN, GAN). Currently, there are many datasets for 2D artificial intelligence learning, but this study provided an opportunity to collect 3D artificial intelligence learning datasets. The research results can be used in the fields of robots, autonomous driving, games, and product design.


Keywords


3D model voxelization; point cloud; 3D vision; AI 3D model learning; mesh 3D dataset

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References


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DOI: https://doi.org/10.32629/jai.v7i1.997

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