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

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 …

This paper presents a new method for estimating normals on unorganized point clouds that
preserves sharp features. It is based on a robust version of the Randomized Hough …

Segmentation is a most important intermediate step in point cloud data processing and
understanding. Covariance statistics based local saliency features from Principal …

Modern remote sensing technologies such as three-dimensional (3D) laser scanners and
image-based 3D scene reconstruction are in increasing demand for applications in civil …

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

This paper presents a novel technique for estimating normals on unorganized point clouds.
Methods from robust statistics are used to detect the best local tangent plane for each point …

This paper proposes robust methods for local planar surface fitting in 3D laser scanning
data. Searching through the literature revealed that many authors frequently used Least …

In this paper, we propose a normal estimation method for unstructured 3D point clouds. In
this method, a feature constraint mechanism called Local Plane Features Constraint (LPFC) …

In this paper, we present a robust normal estimation algorithm based on the low-rank
subspace clustering technique. The main idea is based on the observation that compared …