# estimateEssentialMatrix

Estimate essential matrix from corresponding points in a pair of images

## Syntax

## Description

returns the 3-by-3 essential matrix, `E`

= estimateEssentialMatrix(`matchedPoints1`

,`matchedPoints2`

,`intrinsics`

)`E`

, using the M-estimator sample
consensus (MSAC) algorithm. The input points can be *M*-by-2 matrices of
*M* number of [*x*,*y*] coordinates, or a
`KAZEPoints`

, `SIFTPoints`

,`SURFPoints`

, `MSERRegions`

, `BRISKPoints`

, or `cornerPoints`

object. The
`instinsics`

object contains the intrinsic parameters of the camera used to
take the images.

returns the essential matrix relating two images taken by different cameras.
`E`

= estimateEssentialMatrix(`matchedPoints1`

,`matchedPoints2`

,`intrinsics1`

,`intrinsics2`

)`intrinsics1`

and `intrinsics2`

are `cameraIntrinsics`

objects containing the intrinsic parameters of camera 1 and camera
2 respectively.

`[`

additionally returns an `E`

,`inliersIndex`

]
= estimateEssentialMatrix(___)*M*-by-1 logical vector,
`inliersIndex`

, used to compute the essential matrix. The function sets the
elements of the vector to `true`

when the corresponding point was used to
compute the fundamental matrix. The elements are set to `false`

if they are
not used.

`[`

additionally returns a status code to indicate the validity of points.`E`

,`inliersIndex`

,`status`

]
= estimateEssentialMatrix(___)

```
[___]
= estimateEssentialMatrix(___,
```

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

)`(MaxNumTrials=500)`

sets the maximum number
of random trials for finding outliers to `500`

.

## Examples

## Input Arguments

## Output Arguments

## Tips

Use `estimateEssentialMatrix`

when you know
the camera intrinsics. You can obtain the intrinsics using the Camera Calibrator app. Otherwise,
you can use the `estimateFundamentalMatrix`

function
that does not require camera intrinsics. The fundamental matrix cannot
be estimated from coplanar world points.

## References

[1] Kukelova, Z., M. Bujnak, and T. Pajdla *Polynomial
Eigenvalue Solutions to the 5-pt and 6-pt Relative Pose Problems.* Leeds,
UK: BMVC, 2008.

[2] Nister, D.. “An Efficient Solution
to the Five-Point Relative Pose Problem.” *IEEE Transactions
on Pattern Analysis and Machine Intelligence.*Volume 26,
Issue 6, June 2004.

[3] Torr, P. H. S., and A. Zisserman. “MLESAC:
A New Robust Estimator with Application to Estimating Image Geometry.” *Computer
Vision and Image Understanding.* Volume 78, Issue 1, April
2000, pp. 138-156.

## Extended Capabilities

## Version History

**Introduced in R2016b**