gives a MeshRegion whose gradient best fits the normals at points p1,p2,.

# Details • GradientFittedMesh is also known as Poisson surface reconstruction.
• GradientFittedMesh is typically used to construct closed smooth regions from a set of points with normals.
• • GradientFittedMesh gives a MeshRegion that approximates a geometric region with a scalar indicator function whose gradient best fits the normals ni at the points pi.
• GradientFittedMesh finds the best least-squares approximate solution whose Laplacian equals the divergence of the normals .
• Vertex normals ni for points pi can be specified by Point[{p1,p2,},VertexNormals{n1,n2,}].
• In GradientFittedMesh[{p1,p2,}], normals to the points pti are estimated by computing the least-squares fit plane on the nearest neighboring points.
• GradientFittedMesh takes the same options as MeshRegion, with the following additions and changes:
•  PerformanceGoal \$PerformanceGoal aspects of performance to try to optimize VertexNormals Automatic vertex normals to use

# Examples

open allclose all

## Basic Examples(2)

Reconstruct a sphere from random points:

An oriented point sample of a Beethoven sculpture:

3D reconstructed mesh:

## Scope(2)

It is equivalent to points without normals:

## Options(2)

### VertexNormals(1)

Specify coordinate orientations using VertexNormals:

This is equivalent to passing oriented points:

### PerformanceGoal(1)

Generate a higher-quality mesh:

Emphasize performance, possibly at the cost of quality:

## Applications(1)

Reconstruct a mesh from oriented points in :

The reconstructed mesh:

Basic properties:

Reconstructed meshes are bounded:

Find its area and centroid:

## Possible Issues(1)

GradientFittedMesh only works on 3D points: