Rotation Matrix for Vector v=(a,b,c) by Angle θ | Efficient Computation Method

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SUMMARY

The discussion focuses on computing the rotation matrix for a vector v=(a,b,c) around an angle θ. The participant suggests using Rodrigues' rotation formula as a more efficient method than brute force matrix multiplication. They emphasize the importance of constructing an orthonormal basis perpendicular to the vector v and using it to transform the standard basis for the rotation. A reference link is provided for further details on constructing the rotation matrix.

PREREQUISITES
  • Understanding of rotation matrices in 3D space
  • Familiarity with Rodrigues' rotation formula
  • Knowledge of orthonormal basis concepts
  • Basic linear algebra, including matrix multiplication
NEXT STEPS
  • Research Rodrigues' rotation formula for efficient rotation matrix computation
  • Study the construction of orthonormal bases in 3D space
  • Learn about matrix conjugation and its application in rotation transformations
  • Explore practical examples of rotation matrices in computer graphics
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This discussion is beneficial for mathematicians, computer graphics developers, and anyone involved in 3D modeling or simulations requiring efficient rotation computations.

Silviu
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Hello! I need to find the rotation matrix around a given vector v=(a,b,c), by and angle ##\theta##. I can find an orthonormal basis of the plane perpendicular to v but how can I compute the matrix from this? I think I can do it by brute force, rewriting the orthonormal basis rotated by ##\theta## and doing some matrix multiplications, but is there a more clever and fast way to do it?
Thank you!
 
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I don't think there is any simpler method than "brute force".
 
You might want to research Rodrigues' rotation formula.
 
I guess this is what you are saying but the obviopus way to prceed seems to me to use the basis you have as a map sending the usual orthonormal basis to the new one, and use that map and its inverse to conjugate the (easily written) rotation about the z axis back to your axis.
 

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