public static DMatrixRMaj simpleFundamental( List<AssociatedPair> matches ) {
		// Use the 8-point algorithm since it will work with an arbitrary number of points
		Estimate1ofEpipolar estimateF = FactoryMultiView.fundamental_1(EnumFundamental.LINEAR_8, 0);

		DMatrixRMaj F = new DMatrixRMaj(3,3);
		if( !estimateF.process(matches,F) )
			throw new IllegalArgumentException("Failed");

		// while not done here, this initial linear estimate can be refined using non-linear optimization
		// as was done above.
		return F;
	}
public static DMatrixRMaj simpleFundamental ( List < AssociatedPair > matches ) { Estimate1ofEpipolar estimateF = FactoryMultiView . fundamental_1 ( EnumFundamental . LINEAR_8 , 0 ) ; DMatrixRMaj F = new DMatrixRMaj ( 3 , 3 ) ; if ( ! estimateF . process ( matches , F ) ) throw new IllegalArgumentException ( "Failed" ) ; return F ; }
If the set of associated features are known to be correct, then the fundamental matrix can
be computed directly with a lot less code.  The down side is that this technique is very
sensitive to noise.