1 citations · 2 across the 5 of their papers we have counts for
6 papers
PFF-Net: Patch Feature Fitting for Point Cloud Normal Estimation
Qing Li, Huifang Feng, Kanle Shi +4
Estimating the normal of a point requires constructing a local patch to provide center-surrounding context, but determining the appropriate neighborhood size is difficult when deal…
VA-GS: Enhancing the Geometric Representation of Gaussian Splatting via View Alignment
Qing Li, Huifang Feng, Xun Gong +1
3D Gaussian Splatting has recently emerged as an efficient solution for high-quality and real-time novel view synthesis. However, its capability for accurate surface reconstruction…
Learning Normals of Noisy Points by Local Gradient-Aware Surface Filtering
Qing Li, Huifang Feng, Xun Gong +1
Estimating normals for noisy point clouds is a persistent challenge in 3D geometry processing, particularly for end-to-end oriented normal estimation. Existing methods generally ad…
NeuralGF: Unsupervised Point Normal Estimation by Learning Neural Gradient Function
Qing Li, Huifang Feng, Kanle Shi +4
Normal estimation for 3D point clouds is a fundamental task in 3D geometry processing. The state-of-the-art methods rely on priors of fitting local surfaces learned from normal sup…
Neural Gradient Learning and Optimization for Oriented Point Normal Estimation
Qing Li, Huifang Feng, Kanle Shi +3
We propose Neural Gradient Learning (NGL), a deep learning approach to learn gradient vectors with consistent orientation from 3D point clouds for normal estimation. It has excelle…
Learning Signed Hyper Surfaces for Oriented Point Cloud Normal Estimation
Qing Li, Huifang Feng, Kanle Shi +4
We propose a novel method called SHS-Net for oriented normal estimation of point clouds by learning signed hyper surfaces, which can accurately predict normals with global consiste…