1 code implementation • 13 Oct 2023 • Guangshun Wei, Hao Pan, Shaojie Zhuang, Yuanfeng Zhou, Changjian Li
To solve the non-uniformity of input points, on top of the cross field guided upsampling, we further introduce an iterative strategy that refines the point distribution by moving sparse points onto the desired continuous 3D surface in each iteration.
1 code implementation • 29 May 2023 • Achraf Ben-Hamadou, Oussama Smaoui, Ahmed Rekik, Sergi Pujades, Edmond Boyer, Hoyeon Lim, Minchang Kim, Minkyung Lee, Minyoung Chung, Yeong-Gil Shin, Mathieu Leclercq, Lucia Cevidanes, Juan Carlos Prieto, Shaojie Zhuang, Guangshun Wei, Zhiming Cui, Yuanfeng Zhou, Tudor Dascalu, Bulat Ibragimov, Tae-Hoon Yong, Hong-Gi Ahn, Wan Kim, Jae-Hwan Han, Byungsun Choi, Niels van Nistelrooij, Steven Kempers, Shankeeth Vinayahalingam, Julien Strippoli, Aurélien Thollot, Hugo Setbon, Cyril Trosset, Edouard Ladroit
To address these challenges, the 3DTeethSeg'22 challenge was organized in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2022, with a call for algorithms tackling teeth localization, segmentation, and labeling from intraoral 3D scans.
no code implementations • 22 Mar 2023 • Shenghai Liao, Xuya Liu, Ruyi Han, Shujun Fu, Yuanfeng Zhou, Yuliang Li
A non-convex weighted low-rank decomposition (NC-WLRD) model for LRMA is also proposed to reconstruct all degraded patch matrices grouped by the proposed RwM algorithm.
no code implementations • 8 Nov 2021 • Guangshun Wei, Zhiming Cui, Jie Zhu, Lei Yang, Yuanfeng Zhou, Pradeep Singh, Min Gu, Wenping Wang
Results show that our method can produce tooth landmarks with high accuracy.
no code implementations • 12 Jul 2021 • Ping Wang, Jizong Peng, Marco Pedersoli, Yuanfeng Zhou, Caiming Zhang, Christian Desrosiers
Despite their outstanding accuracy, semi-supervised segmentation methods based on deep neural networks can still yield predictions that are considered anatomically impossible by clinicians, for instance, containing holes or disconnected regions.
1 code implementation • 31 Oct 2020 • Ping Wang, Jizong Peng, Marco Pedersoli, Yuanfeng Zhou, Caiming Zhang, Christian Desrosiers
Moreover, to encourage predictions from different networks to be both consistent and confident, we enhance this generalized JSD loss with an uncertainty regularizer based on entropy.
no code implementations • IEEE 2019 • Xiao Pan, Yuanfeng Zhou, Zhonggui Chen, Caiming Zhang
Abstract—Superpixel generation, which is an essential step in many image processing applications, has attracted increasing attention from researchers.