Estimating normals with globally consistent orientations for a raw point cloud has many
downstream geometry processing applications. Despite tremendous efforts in the past …

Techniques from sparse representation have been successfully applied in many areas like
digital image processing, computer vision and pattern recognition in the past ten years …

We present an algorithm for the restoration of noisy point cloud data, termed Moving Robust
Principal Components Analysis (MRPCA). We model the point cloud as a collection of …

Point cloud filtering, the main bottleneck of which is removing noise (outliers) while
preserving geometric features, is a fundamental problem in 3D field. The two-step schemes …

Edges provide important visual information in scene surfaces. The need for fast and robust
feature extraction from 3D data is nowadays fostered by the widespread availability of cheap …

Normal estimation in point clouds is a crucial first step for numerous algorithms, from surface
reconstruction and scene understanding to rendering. A recurrent issue when estimating …

Point normal, as an intrinsic geometric property of 3D objects, not only serves conventional
geometric tasks such as surface consolidation and reconstruction, but also facilitates cutting …

We propose a geometry-guided neural network architecture for robust and detail-preserving
surface normal estimation for unstructured point clouds. Previous deep normal estimators …

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 robust normal estimation method for both point clouds and meshes using a
low rank matrix approximation algorithm. First, we compute a local isotropic structure for …