# detectISSFeatures

## Syntax

## Description

`___ = detectISSFeatures(`

specifies options using one or more name-value arguments in addition to any combination of
output arguments from previous syntaxes. For example,
`ptCloud`

,`Name=Value`

)`detectISSFeatures(ptCloud,Radius=0.05)`

computes the ISS saliency within
a 0.05 m radius around each point when identifying the feature points.

## Examples

## Input Arguments

## Output Arguments

## Algorithms

Intrinsic shape signatures (ISS) are a method of 3-D shape representation. ISS feature points are rich in 3-D structural variations in their neighbourhood. This method has applications in modeling, visualization, and classification of 3-D point clouds.

To detect ISS feature points in a point cloud, the `detectISSFeatures`

function follows these steps.

Computes a point scatter matrix within the specified

`Radius`

around each point.Computes the eigenvalues

*λ*,_{1}*λ*, and_{2}*λ*in decreasing order of magnitude for the scatter matrix. These eigenvalues represent a direction in the 3-D space based on the number of point position variations._{3}Using the eigenvalues, the function defines a view-independent intrinsic reference frame with the principal

*x*-,*y*-,*z*-axes.Uses

*λ*/_{2}*λ*,_{1}*λ*/_{3}*λ*as criteria to avoid the points with similar spatial spread along the principal axes while detecting feature points. You can specify eigenvalue ratios for_{2}*λ*/_{2}*λ*and_{1}*λ*/_{3}*λ*using the_{2}`MaxGamma21`

and`MaxGamma32`

arguments, respectively.Computes the ISS saliency for each point using the smallest eigenvalue,

*λ*. ISS feature point is the point with maximum ISS saliency within the specified radius around each point._{3}You can further process these feature points to match point clouds, estimate pose transformations, and detect 3-D objects.

## Version History

**Introduced in R2022a**