1 citations · 2 across the 4 of their papers we have counts for
5 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…
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…