Citation

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 (Unpublished)

Text 30_AJAYI EBENEZER AKINYEMI_FIST.pdf - Submitted Version Restricted to Registered users only Download (781kB)

Abstract

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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