3D Shapes Generation Using Deep Learning


Akinyemi, Ajayi Ebenezer and Lim, Kian Ming and Chong, Siew Chin (2022) 3D Shapes Generation Using Deep Learning. In: Postgraduate Colloquium December 2022, 1-15 December 2022, Multimedia University, Malaysia. (Unpublished)

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Advances in robotics technology, artificial intelligence, 3D game, virtual, and augmented reality have boosted 3D shape generation via 3D deep learning. - In recent years, generative modeling with the CNN framework has been investigated to generate voxel and point cloud-based 3D shapes, but with many limitations. - The need to generate 3D shapes from the 2D image using the CNN framework promotes implicit data representation.

Item Type: Conference or Workshop Item (Poster)
Uncontrolled Keywords: Deep learning, Machine learning
Subjects: Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions: Faculty of Information Science and Technology (FIST)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 16 Dec 2022 02:48
Last Modified: 16 Dec 2022 02:48
URII: http://shdl.mmu.edu.my/id/eprint/10894


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