Self-parameterization based multi-resolution mesh convolution networks
This paper addresses the challenges of designing mesh convolution neural networks for 3D mesh dense prediction. While deep learning has achieved remarkable success in image dense prediction tasks, directly applying or extending these methods to irregular graph data, such as 3D surface meshes, is non...
Saved in:
Main Authors: | Shi, Hezi, Jiang, Luo, Zheng, Jianmin, Zeng, Jun |
---|---|
Other Authors: | School of Computer Science and Engineering |
Format: | Article |
Language: | English |
Published: |
2023
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/169922 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Multi-resolution attention convolutional neural network for crowd counting
by: Zhang, Youmei, et al.
Published: (2020) -
Multi-channel convolutional neural network based 3D object detection for indoor robot environmental perception
by: Wang, Li, et al.
Published: (2019) -
L₀-regularization based material design for hexahedral mesh models
by: Li, Haoxiang, et al.
Published: (2022) -
Engineering wireless mesh networks
by: Girard, Andre., et al.
Published: (2009) -
Seamless simplification of multi-chart textured meshes with adaptively updated correspondence
by: Zhang, Wenjing, et al.
Published: (2022)