Abubakar Sulaiman Gezawa1, Qicong Wang1,2, Haruna Chiroma3, Yunqi Lei1,*
CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.2, pp. 1745-1763, 2023, DOI:10.32604/cmes.2022.021351
Abstract In the shape analysis community, decomposing a 3D shape into meaningful parts has become a topic of interest. 3D
model segmentation is largely used in tasks such as shape deformation, shape partial matching, skeleton extraction,
shape correspondence, shape annotation and texture mapping. Numerous approaches have attempted to provide
better segmentation solutions; however, the majority of the previous techniques used handcrafted features, which
are usually focused on a particular attribute of 3D objects and so are difficult to generalize. In this paper, we propose
a three-stage approach for using Multi-view recurrent neural network to automatically segment a 3D shape into
visually… More >
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