Globally Consistent Normal Orientation for Point Clouds by Regularizing the Winding-Number Field

Mendable consistent orientation of point clouds

Consistent normal orientation is challenging in the presence of noise, non-uniformities and thin sharp features. None of any existing local or global methods is capable of orienting all point cloud models consistently, and none of them offers a mechanism to rectify the inconsistent normals. In this paper, we present a new normal orientation method based on the multi-source propagation technique...

Consistent Propagation of Normal Orientations in Point Clouds

Many algorithms for point cloud processing especially surface reconstruction rely on normal information available at each point. Normal directions are typically taken from a local tangent plane approximation which is obtained by fitting a surface model to the neighboring point samples. While the direction can be estimated locally, finding a consistent normal orientation over the whole surface i...

Anisotropic Normal Estimation for Sparse Point Clouds

This technical report presents a normal estimation method for point clouds with low sampling density and sharp features. To achieve the best trade-off between quality and performance, normal of a point on smooth regions is estimated using a isotropic neighborhood with constant size; but normal of a point near features are evaluated with an anisotropic neighborhood from which the local tangent p...

Learning Globally Consistent Maps by Relaxation

Regularizing Lasso: a Consistent Variable Selection Method

Table 1 provides the average computational time (in minutes) for the eight methods under the simulation settings. SIS clearly requires the least computational effort, whereas RLASSO as well as Scout require much longer computational time. But all methods except RLASSO(CLIME) can be computed under a reasonable amount of time for p = 5000 and n = 100. RLASSO(CLIME) takes much longer because of in...