17 citations · 80 across the 15 of their papers we have counts for
29 papers
NeAF: Learning Neural Angle Fields for Point Normal Estimation
Shujuan Li, Junsheng Zhou, Baorui Ma +2
Normal estimation for unstructured point clouds is an important task in 3D computer vision. Current methods achieve encouraging results by mapping local patches to normal vectors o…
HSurf-Net: Normal Estimation for 3D Point Clouds by Learning Hyper Surfaces
Qing Li, Yu-Shen Liu, Jin-San Cheng +3
We propose a novel normal estimation method called HSurf-Net, which can accurately predict normals from point clouds with noise and density variations. Previous methods focus on le…
Surface Reconstruction from Point Clouds by Learning Predictive Context Priors
Baorui Ma, Yu-Shen Liu, Matthias Zwicker +1
Surface reconstruction from point clouds is vital for 3D computer vision. State-of-the-art methods leverage large datasets to first learn local context priors that are represented…
Reconstructing Surfaces for Sparse Point Clouds with On-Surface Priors
Baorui Ma, Yu-Shen Liu, Zhizhong Han
It is an important task to reconstruct surfaces from 3D point clouds. Current methods are able to reconstruct surfaces by learning Signed Distance Functions (SDFs) from single poin…
Learning Deep Implicit Functions for 3D Shapes with Dynamic Code Clouds
Tianyang Li, Xin Wen, Yu-Shen Liu +2
Deep Implicit Function (DIF) has gained popularity as an efficient 3D shape representation. To capture geometry details, current methods usually learn DIF using local latent codes,…
3D Shape Reconstruction from 2D Images with Disentangled Attribute Flow
Xin Wen, Junsheng Zhou, Yu-Shen Liu +2
Reconstructing 3D shape from a single 2D image is a challenging task, which needs to estimate the detailed 3D structures based on the semantic attributes from 2D image. So far, mos…