57 citations · 77 across the 7 of their papers we have counts for
7 papers
SPCNet: Stepwise Point Cloud Completion Network
Fei Hu, Honghua Chen, Xuequan Lu +5
How will you repair a physical object with large missings? You may first recover its global yet coarse shape and stepwise increase its local details. We are motivated to imitate th…
Geometric and Learning-based Mesh Denoising: A Comprehensive Survey
Honghua Chen, Mingqiang Wei, Jun Wang
Mesh denoising is a fundamental problem in digital geometry processing. It seeks to remove surface noise, while preserving surface intrinsic signals as accurately as possible. Whil…
MODNet: Multi-offset Point Cloud Denoising Network Customized for Multi-scale Patches
Anyi Huang, Qian Xie, Zhoutao Wang +3
The intricacy of 3D surfaces often results cutting-edge point cloud denoising (PCD) models in surface degradation including remnant noise, wrongly-removed geometric details. Althou…
UTOPIC: Uncertainty-aware Overlap Prediction Network for Partial Point Cloud Registration
Zhilei Chen, Honghua Chen, Lina Gong +5
High-confidence overlap prediction and accurate correspondences are critical for cutting-edge models to align paired point clouds in a partial-to-partial manner. However, there inh…
CSDN: Cross-modal Shape-transfer Dual-refinement Network for Point Cloud Completion
Zhe Zhu, Liangliang Nan, Haoran Xie +4
How will you repair a physical object with some missings? You may imagine its original shape from previously captured images, recover its overall (global) but coarse shape first, a…
ImLoveNet: Misaligned Image-supported Registration Network for Low-overlap Point Cloud Pairs
Honghua Chen, Zeyong Wei, Yabin Xu +2
Low-overlap regions between paired point clouds make the captured features very low-confidence, leading cutting edge models to point cloud registration with poor quality. Beyond th…
AGConv: Adaptive Graph Convolution on 3D Point Clouds
Mingqiang Wei, Zeyong Wei, Haoran Zhou +9
Convolution on 3D point clouds is widely researched yet far from perfect in geometric deep learning. The traditional wisdom of convolution characterises feature correspondences ind…