More efficient rigid 3D transformation in Java












2















I want to calculate a rigid 3D transformation between two sets of 3D points. I googled myself, found no suitable implementation and implemented it myself with the help of the Apache Commons Math library, based on this guide. The implementation can be seen below:



  public static RigidTransformation3dAnswer computeRigidTransformation3D(RealMatrix src,
RealMatrix dst) {
if (src.getRowDimension() == dst.getRowDimension() && src.getColumnDimension() == dst
.getColumnDimension()) {

int n = src.getRowDimension();
RealMatrix centroidSrc = computeCentroid(src);
RealMatrix centroidDst = computeCentroid(dst);

RealMatrix aa = src.subtract(tile(centroidSrc, n));
RealMatrix bb = dst.subtract(tile(centroidDst, n));

RealMatrix h = aa.transpose().multiply(bb);
SingularValueDecomposition singularValueDecomposition = new SingularValueDecomposition(h);

RealMatrix u = singularValueDecomposition.getU();
RealMatrix vt = singularValueDecomposition.getVT();
RealMatrix rotationMatrix = vt.transpose().multiply(u.transpose());

if (new LUDecomposition(rotationMatrix).getDeterminant() < 0) {
vt.setColumn(2, vt.getColumnVector(2).mapMultiplyToSelf(-1).toArray());
rotationMatrix = vt.transpose().multiply(u.transpose());
}

RealMatrix transpose = (rotationMatrix.scalarMultiply(-1).multiply(centroidSrc.transpose()))
.add(centroidDst.transpose());

RigidTransformation3dAnswer answer = new RigidTransformation3dAnswer();
answer.setRotationMatrix(rotationMatrix);
answer.setTranslationMatrix(transpose);
return answer;
}
return null;
}

private static RealMatrix tile(RealMatrix a, int n) {
RealMatrix realMatrix = new Array2DRowRealMatrix(n, a.getColumnDimension());
for (int i = 0; i < n; i++) {
realMatrix.setEntry(i, 0, a.getEntry(0, 0));
realMatrix.setEntry(i, 1, a.getEntry(0, 1));
realMatrix.setEntry(i, 2, a.getEntry(0, 2));
}
return realMatrix;
}

private static RealMatrix computeCentroid(RealMatrix mat) {
double sumX = 0;
double sumY = 0;
double sumZ = 0;
double returnArray = new double[1][3];
for (int i = 0; i < mat.getRowDimension(); i++) {
double a = mat.getEntry(i, 0);
sumX = sumX + a;
a = mat.getEntry(i, 1);
sumY = sumY + a;
a = mat.getEntry(i, 2);
sumZ = sumZ + a;
}
double centroidX = sumX / (double) mat.getRowDimension();
double centroidY = sumY / (double) mat.getRowDimension();
double centroidZ = sumZ / (double) mat.getRowDimension();
returnArray[0][0] = centroidX;
returnArray[0][1] = centroidY;
returnArray[0][2] = centroidZ;
return new Array2DRowRealMatrix(returnArray);
}


Although this implementation works perfectly, it is not very efficient and takes around 50 ms on desktop, which is not suitable because I want to use it in an Android application.
So here are three question:



a) is there a more efficient library or framework method to use to compute a rigid 3D transformation?



b) If not, is there a heuristic, and therefore more efficient, implementation of such a transformation, if, for example, I exclude the translation and only want rotation around the Z (vertical) axis?



c) If neither of those two points has an answer, is there a way to improve my code efficiency-wise?










share|improve this question


















  • 2





    In src.subtract(tile(centroidSrc, n)) you create a new matrix just for the subtraction and then a new one for the result, even though you don't use src later. It would probably be faster to write a method void subtractFromEachRow(RealMatrix minuend, RealMatrix subtrahendRow) that does the subtraction without creating any new matrices. Modify src using the addToEntry method.

    – Socowi
    Nov 22 '18 at 14:26













  • Also vt.transpose().multiply(u.transpose()) is the same as u.multiply(vt).transpose(), but the latter is faster. However, since vt and u are small, that shouldn't change much.

    – Socowi
    Nov 22 '18 at 14:32













  • How many point pairs do you have?

    – Nico Schertler
    Nov 22 '18 at 17:36











  • Normally around 7, 10 is the maximum

    – Noltibus
    Nov 22 '18 at 18:14






  • 1





    Ok, then 50ms is very slow, indeed. I think the most important factor is the allocation of new memory as @Socowi pointed out. If that does not help, you need to profile where the time is spent. Minor thing: You can calculate the determinant of a 3x3 matrix directly and do not need to calculate a LU decomposition.

    – Nico Schertler
    Nov 22 '18 at 18:37
















2















I want to calculate a rigid 3D transformation between two sets of 3D points. I googled myself, found no suitable implementation and implemented it myself with the help of the Apache Commons Math library, based on this guide. The implementation can be seen below:



  public static RigidTransformation3dAnswer computeRigidTransformation3D(RealMatrix src,
RealMatrix dst) {
if (src.getRowDimension() == dst.getRowDimension() && src.getColumnDimension() == dst
.getColumnDimension()) {

int n = src.getRowDimension();
RealMatrix centroidSrc = computeCentroid(src);
RealMatrix centroidDst = computeCentroid(dst);

RealMatrix aa = src.subtract(tile(centroidSrc, n));
RealMatrix bb = dst.subtract(tile(centroidDst, n));

RealMatrix h = aa.transpose().multiply(bb);
SingularValueDecomposition singularValueDecomposition = new SingularValueDecomposition(h);

RealMatrix u = singularValueDecomposition.getU();
RealMatrix vt = singularValueDecomposition.getVT();
RealMatrix rotationMatrix = vt.transpose().multiply(u.transpose());

if (new LUDecomposition(rotationMatrix).getDeterminant() < 0) {
vt.setColumn(2, vt.getColumnVector(2).mapMultiplyToSelf(-1).toArray());
rotationMatrix = vt.transpose().multiply(u.transpose());
}

RealMatrix transpose = (rotationMatrix.scalarMultiply(-1).multiply(centroidSrc.transpose()))
.add(centroidDst.transpose());

RigidTransformation3dAnswer answer = new RigidTransformation3dAnswer();
answer.setRotationMatrix(rotationMatrix);
answer.setTranslationMatrix(transpose);
return answer;
}
return null;
}

private static RealMatrix tile(RealMatrix a, int n) {
RealMatrix realMatrix = new Array2DRowRealMatrix(n, a.getColumnDimension());
for (int i = 0; i < n; i++) {
realMatrix.setEntry(i, 0, a.getEntry(0, 0));
realMatrix.setEntry(i, 1, a.getEntry(0, 1));
realMatrix.setEntry(i, 2, a.getEntry(0, 2));
}
return realMatrix;
}

private static RealMatrix computeCentroid(RealMatrix mat) {
double sumX = 0;
double sumY = 0;
double sumZ = 0;
double returnArray = new double[1][3];
for (int i = 0; i < mat.getRowDimension(); i++) {
double a = mat.getEntry(i, 0);
sumX = sumX + a;
a = mat.getEntry(i, 1);
sumY = sumY + a;
a = mat.getEntry(i, 2);
sumZ = sumZ + a;
}
double centroidX = sumX / (double) mat.getRowDimension();
double centroidY = sumY / (double) mat.getRowDimension();
double centroidZ = sumZ / (double) mat.getRowDimension();
returnArray[0][0] = centroidX;
returnArray[0][1] = centroidY;
returnArray[0][2] = centroidZ;
return new Array2DRowRealMatrix(returnArray);
}


Although this implementation works perfectly, it is not very efficient and takes around 50 ms on desktop, which is not suitable because I want to use it in an Android application.
So here are three question:



a) is there a more efficient library or framework method to use to compute a rigid 3D transformation?



b) If not, is there a heuristic, and therefore more efficient, implementation of such a transformation, if, for example, I exclude the translation and only want rotation around the Z (vertical) axis?



c) If neither of those two points has an answer, is there a way to improve my code efficiency-wise?










share|improve this question


















  • 2





    In src.subtract(tile(centroidSrc, n)) you create a new matrix just for the subtraction and then a new one for the result, even though you don't use src later. It would probably be faster to write a method void subtractFromEachRow(RealMatrix minuend, RealMatrix subtrahendRow) that does the subtraction without creating any new matrices. Modify src using the addToEntry method.

    – Socowi
    Nov 22 '18 at 14:26













  • Also vt.transpose().multiply(u.transpose()) is the same as u.multiply(vt).transpose(), but the latter is faster. However, since vt and u are small, that shouldn't change much.

    – Socowi
    Nov 22 '18 at 14:32













  • How many point pairs do you have?

    – Nico Schertler
    Nov 22 '18 at 17:36











  • Normally around 7, 10 is the maximum

    – Noltibus
    Nov 22 '18 at 18:14






  • 1





    Ok, then 50ms is very slow, indeed. I think the most important factor is the allocation of new memory as @Socowi pointed out. If that does not help, you need to profile where the time is spent. Minor thing: You can calculate the determinant of a 3x3 matrix directly and do not need to calculate a LU decomposition.

    – Nico Schertler
    Nov 22 '18 at 18:37














2












2








2








I want to calculate a rigid 3D transformation between two sets of 3D points. I googled myself, found no suitable implementation and implemented it myself with the help of the Apache Commons Math library, based on this guide. The implementation can be seen below:



  public static RigidTransformation3dAnswer computeRigidTransformation3D(RealMatrix src,
RealMatrix dst) {
if (src.getRowDimension() == dst.getRowDimension() && src.getColumnDimension() == dst
.getColumnDimension()) {

int n = src.getRowDimension();
RealMatrix centroidSrc = computeCentroid(src);
RealMatrix centroidDst = computeCentroid(dst);

RealMatrix aa = src.subtract(tile(centroidSrc, n));
RealMatrix bb = dst.subtract(tile(centroidDst, n));

RealMatrix h = aa.transpose().multiply(bb);
SingularValueDecomposition singularValueDecomposition = new SingularValueDecomposition(h);

RealMatrix u = singularValueDecomposition.getU();
RealMatrix vt = singularValueDecomposition.getVT();
RealMatrix rotationMatrix = vt.transpose().multiply(u.transpose());

if (new LUDecomposition(rotationMatrix).getDeterminant() < 0) {
vt.setColumn(2, vt.getColumnVector(2).mapMultiplyToSelf(-1).toArray());
rotationMatrix = vt.transpose().multiply(u.transpose());
}

RealMatrix transpose = (rotationMatrix.scalarMultiply(-1).multiply(centroidSrc.transpose()))
.add(centroidDst.transpose());

RigidTransformation3dAnswer answer = new RigidTransformation3dAnswer();
answer.setRotationMatrix(rotationMatrix);
answer.setTranslationMatrix(transpose);
return answer;
}
return null;
}

private static RealMatrix tile(RealMatrix a, int n) {
RealMatrix realMatrix = new Array2DRowRealMatrix(n, a.getColumnDimension());
for (int i = 0; i < n; i++) {
realMatrix.setEntry(i, 0, a.getEntry(0, 0));
realMatrix.setEntry(i, 1, a.getEntry(0, 1));
realMatrix.setEntry(i, 2, a.getEntry(0, 2));
}
return realMatrix;
}

private static RealMatrix computeCentroid(RealMatrix mat) {
double sumX = 0;
double sumY = 0;
double sumZ = 0;
double returnArray = new double[1][3];
for (int i = 0; i < mat.getRowDimension(); i++) {
double a = mat.getEntry(i, 0);
sumX = sumX + a;
a = mat.getEntry(i, 1);
sumY = sumY + a;
a = mat.getEntry(i, 2);
sumZ = sumZ + a;
}
double centroidX = sumX / (double) mat.getRowDimension();
double centroidY = sumY / (double) mat.getRowDimension();
double centroidZ = sumZ / (double) mat.getRowDimension();
returnArray[0][0] = centroidX;
returnArray[0][1] = centroidY;
returnArray[0][2] = centroidZ;
return new Array2DRowRealMatrix(returnArray);
}


Although this implementation works perfectly, it is not very efficient and takes around 50 ms on desktop, which is not suitable because I want to use it in an Android application.
So here are three question:



a) is there a more efficient library or framework method to use to compute a rigid 3D transformation?



b) If not, is there a heuristic, and therefore more efficient, implementation of such a transformation, if, for example, I exclude the translation and only want rotation around the Z (vertical) axis?



c) If neither of those two points has an answer, is there a way to improve my code efficiency-wise?










share|improve this question














I want to calculate a rigid 3D transformation between two sets of 3D points. I googled myself, found no suitable implementation and implemented it myself with the help of the Apache Commons Math library, based on this guide. The implementation can be seen below:



  public static RigidTransformation3dAnswer computeRigidTransformation3D(RealMatrix src,
RealMatrix dst) {
if (src.getRowDimension() == dst.getRowDimension() && src.getColumnDimension() == dst
.getColumnDimension()) {

int n = src.getRowDimension();
RealMatrix centroidSrc = computeCentroid(src);
RealMatrix centroidDst = computeCentroid(dst);

RealMatrix aa = src.subtract(tile(centroidSrc, n));
RealMatrix bb = dst.subtract(tile(centroidDst, n));

RealMatrix h = aa.transpose().multiply(bb);
SingularValueDecomposition singularValueDecomposition = new SingularValueDecomposition(h);

RealMatrix u = singularValueDecomposition.getU();
RealMatrix vt = singularValueDecomposition.getVT();
RealMatrix rotationMatrix = vt.transpose().multiply(u.transpose());

if (new LUDecomposition(rotationMatrix).getDeterminant() < 0) {
vt.setColumn(2, vt.getColumnVector(2).mapMultiplyToSelf(-1).toArray());
rotationMatrix = vt.transpose().multiply(u.transpose());
}

RealMatrix transpose = (rotationMatrix.scalarMultiply(-1).multiply(centroidSrc.transpose()))
.add(centroidDst.transpose());

RigidTransformation3dAnswer answer = new RigidTransformation3dAnswer();
answer.setRotationMatrix(rotationMatrix);
answer.setTranslationMatrix(transpose);
return answer;
}
return null;
}

private static RealMatrix tile(RealMatrix a, int n) {
RealMatrix realMatrix = new Array2DRowRealMatrix(n, a.getColumnDimension());
for (int i = 0; i < n; i++) {
realMatrix.setEntry(i, 0, a.getEntry(0, 0));
realMatrix.setEntry(i, 1, a.getEntry(0, 1));
realMatrix.setEntry(i, 2, a.getEntry(0, 2));
}
return realMatrix;
}

private static RealMatrix computeCentroid(RealMatrix mat) {
double sumX = 0;
double sumY = 0;
double sumZ = 0;
double returnArray = new double[1][3];
for (int i = 0; i < mat.getRowDimension(); i++) {
double a = mat.getEntry(i, 0);
sumX = sumX + a;
a = mat.getEntry(i, 1);
sumY = sumY + a;
a = mat.getEntry(i, 2);
sumZ = sumZ + a;
}
double centroidX = sumX / (double) mat.getRowDimension();
double centroidY = sumY / (double) mat.getRowDimension();
double centroidZ = sumZ / (double) mat.getRowDimension();
returnArray[0][0] = centroidX;
returnArray[0][1] = centroidY;
returnArray[0][2] = centroidZ;
return new Array2DRowRealMatrix(returnArray);
}


Although this implementation works perfectly, it is not very efficient and takes around 50 ms on desktop, which is not suitable because I want to use it in an Android application.
So here are three question:



a) is there a more efficient library or framework method to use to compute a rigid 3D transformation?



b) If not, is there a heuristic, and therefore more efficient, implementation of such a transformation, if, for example, I exclude the translation and only want rotation around the Z (vertical) axis?



c) If neither of those two points has an answer, is there a way to improve my code efficiency-wise?







java math optimization geometry






share|improve this question













share|improve this question











share|improve this question




share|improve this question










asked Nov 22 '18 at 13:33









NoltibusNoltibus

18515




18515








  • 2





    In src.subtract(tile(centroidSrc, n)) you create a new matrix just for the subtraction and then a new one for the result, even though you don't use src later. It would probably be faster to write a method void subtractFromEachRow(RealMatrix minuend, RealMatrix subtrahendRow) that does the subtraction without creating any new matrices. Modify src using the addToEntry method.

    – Socowi
    Nov 22 '18 at 14:26













  • Also vt.transpose().multiply(u.transpose()) is the same as u.multiply(vt).transpose(), but the latter is faster. However, since vt and u are small, that shouldn't change much.

    – Socowi
    Nov 22 '18 at 14:32













  • How many point pairs do you have?

    – Nico Schertler
    Nov 22 '18 at 17:36











  • Normally around 7, 10 is the maximum

    – Noltibus
    Nov 22 '18 at 18:14






  • 1





    Ok, then 50ms is very slow, indeed. I think the most important factor is the allocation of new memory as @Socowi pointed out. If that does not help, you need to profile where the time is spent. Minor thing: You can calculate the determinant of a 3x3 matrix directly and do not need to calculate a LU decomposition.

    – Nico Schertler
    Nov 22 '18 at 18:37














  • 2





    In src.subtract(tile(centroidSrc, n)) you create a new matrix just for the subtraction and then a new one for the result, even though you don't use src later. It would probably be faster to write a method void subtractFromEachRow(RealMatrix minuend, RealMatrix subtrahendRow) that does the subtraction without creating any new matrices. Modify src using the addToEntry method.

    – Socowi
    Nov 22 '18 at 14:26













  • Also vt.transpose().multiply(u.transpose()) is the same as u.multiply(vt).transpose(), but the latter is faster. However, since vt and u are small, that shouldn't change much.

    – Socowi
    Nov 22 '18 at 14:32













  • How many point pairs do you have?

    – Nico Schertler
    Nov 22 '18 at 17:36











  • Normally around 7, 10 is the maximum

    – Noltibus
    Nov 22 '18 at 18:14






  • 1





    Ok, then 50ms is very slow, indeed. I think the most important factor is the allocation of new memory as @Socowi pointed out. If that does not help, you need to profile where the time is spent. Minor thing: You can calculate the determinant of a 3x3 matrix directly and do not need to calculate a LU decomposition.

    – Nico Schertler
    Nov 22 '18 at 18:37








2




2





In src.subtract(tile(centroidSrc, n)) you create a new matrix just for the subtraction and then a new one for the result, even though you don't use src later. It would probably be faster to write a method void subtractFromEachRow(RealMatrix minuend, RealMatrix subtrahendRow) that does the subtraction without creating any new matrices. Modify src using the addToEntry method.

– Socowi
Nov 22 '18 at 14:26







In src.subtract(tile(centroidSrc, n)) you create a new matrix just for the subtraction and then a new one for the result, even though you don't use src later. It would probably be faster to write a method void subtractFromEachRow(RealMatrix minuend, RealMatrix subtrahendRow) that does the subtraction without creating any new matrices. Modify src using the addToEntry method.

– Socowi
Nov 22 '18 at 14:26















Also vt.transpose().multiply(u.transpose()) is the same as u.multiply(vt).transpose(), but the latter is faster. However, since vt and u are small, that shouldn't change much.

– Socowi
Nov 22 '18 at 14:32







Also vt.transpose().multiply(u.transpose()) is the same as u.multiply(vt).transpose(), but the latter is faster. However, since vt and u are small, that shouldn't change much.

– Socowi
Nov 22 '18 at 14:32















How many point pairs do you have?

– Nico Schertler
Nov 22 '18 at 17:36





How many point pairs do you have?

– Nico Schertler
Nov 22 '18 at 17:36













Normally around 7, 10 is the maximum

– Noltibus
Nov 22 '18 at 18:14





Normally around 7, 10 is the maximum

– Noltibus
Nov 22 '18 at 18:14




1




1





Ok, then 50ms is very slow, indeed. I think the most important factor is the allocation of new memory as @Socowi pointed out. If that does not help, you need to profile where the time is spent. Minor thing: You can calculate the determinant of a 3x3 matrix directly and do not need to calculate a LU decomposition.

– Nico Schertler
Nov 22 '18 at 18:37





Ok, then 50ms is very slow, indeed. I think the most important factor is the allocation of new memory as @Socowi pointed out. If that does not help, you need to profile where the time is spent. Minor thing: You can calculate the determinant of a 3x3 matrix directly and do not need to calculate a LU decomposition.

– Nico Schertler
Nov 22 '18 at 18:37












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