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Explain how to use Error Quadrics to compute the closest point to a set of planes!
We can represent a plane by

if
and a point by

Then, the distance from
to the plane is given by

Given a set of planes
, we want to find a point
which minimizes the squared distances to all planes:





The matrices
are called fundamental error quadrics and are computed as

From this definition, we see that the error quadrics of two sets of planes
,
can simply be merged into one error quadric by adding them:

Solution Approach 1
In order to find a minimum solution for
, we can find the eigenvector to the smallest eigenvalue of
.
Solution Approach 2

By computing

we obtain the linear system


Which is easier to solve than computing the eigenvectors and eigenvalues of
.

if

and a point by

Then, the distance from


Given a set of planes







The matrices


From this definition, we see that the error quadrics of two sets of planes



Solution Approach 1
In order to find a minimum solution for


Solution Approach 2

By computing

we obtain the linear system


Which is easier to solve than computing the eigenvectors and eigenvalues of


Karteninfo:
Autor: janisborn
Oberthema: Informatik
Thema: Computergrafik
Schule / Uni: RWTH Aachen
Ort: Aachen
Veröffentlicht: 18.05.2022