The quality of point clouds is often limited by noise introduced during their capture process.
Consequently, a fundamental 3D vision task is the removal of noise, known as point cloud …

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 …

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 …

Learning signed distance functions (SDFs) from point clouds is an important task in 3D
computer vision. However, without ground truth signed distances, point normals or clean …

Most existing approaches for point cloud normal estimation aim to locally fit a geometric
surface and calculate the normal from the fitted surface. Recently, learning-based methods …

The representation quantifies the geometric shape and topology of a building is a necessary
procedure for many urban planning applications. A sharp line framework is a high-level …

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 …

We propose a precise and efficient normal estimation method that can deal with noise and
nonuniform density for unstructured 3D point clouds. Unlike existing approaches that directly …