使用 Python 从 3D 中的六个点确定齐次仿射变换矩阵

我得到了三个点的位置：

p1 = [1.0, 1.0, 1.0]
p2 = [1.0, 2.0, 1.0]
p3 = [1.0, 1.0, 2.0]

以及它们转化后的对应物：

p1_prime = [2.414213562373094,  5.732050807568877, 0.7320508075688767]
p2_prime = [2.7677669529663684, 6.665063509461097, 0.6650635094610956]
p3_prime = [2.7677669529663675, 5.665063509461096, 1.6650635094610962]

仿射变换矩阵的形式为

trans_mat = np.array([[…, …, …, …],
                      […, …, …, …],
                      […, …, …, …],
                      […, …, …, …]])

这样与

import numpy as np

def transform_pt(point, trans_mat):
    a  = np.array([point[0], point[1], point[2], 1])
    ap = np.dot(a, trans_mat)[:3]
    return [ap[0], ap[1], ap[2]]

你会得到：

transform_pt(p1, trans_mat) == p1_prime
transform_pt(p2, trans_mat) == p2_prime
transform_pt(p3, trans_mat) == p3_prime

假设变换是齐次的（仅包含旋转和平移），我如何确定这个变换矩阵？

从 CAD 程序中，我知道矩阵是：

trans_mat = np.array([[0.866025403784, -0.353553390593, -0.353553390593, 0],
                      [0.353553390593,  0.933012701892, -0.066987298108, 0],
                      [0.353553390593, -0.066987298108,  0.933012701892, 0],
                      [0.841081377402,  5.219578794378,  0.219578794378, 1]])

我想知道如何找到这个。

仅六个点不足以唯一地确定仿射变换。但是，根据您之前在问题中提出的问题（在删除之前不久）以及你的评论 https://stackoverflow.com/questions/27546081/determining-transformation-matrix-from-six-points-in-python/27547597#comment43823906_27547597，看来您不仅仅是在寻找仿射变换，而是在寻找齐次仿射变换.

罗布约翰的回答 https://math.stackexchange.com/a/222170提供问题的解决方案。虽然它解决了一个有很多点的更一般的问题，但 6 个点的解决方案可以在答案的最底部找到。我将在这里以对程序员更友好的格式转录它：

import numpy as np

def recover_homogenous_affine_transformation(p, p_prime):
    '''
    Find the unique homogeneous affine transformation that
    maps a set of 3 points to another set of 3 points in 3D
    space:

        p_prime == np.dot(p, R) + t

    where `R` is an unknown rotation matrix, `t` is an unknown
    translation vector, and `p` and `p_prime` are the original
    and transformed set of points stored as row vectors:

        p       = np.array((p1,       p2,       p3))
        p_prime = np.array((p1_prime, p2_prime, p3_prime))

    The result of this function is an augmented 4-by-4
    matrix `A` that represents this affine transformation:

        np.column_stack((p_prime, (1, 1, 1))) == \
            np.dot(np.column_stack((p, (1, 1, 1))), A)

    Source: https://math.stackexchange.com/a/222170 (robjohn)
    '''

    # construct intermediate matrix
    Q       = p[1:]       - p[0]
    Q_prime = p_prime[1:] - p_prime[0]

    # calculate rotation matrix
    R = np.dot(np.linalg.inv(np.row_stack((Q, np.cross(*Q)))),
               np.row_stack((Q_prime, np.cross(*Q_prime))))

    # calculate translation vector
    t = p_prime[0] - np.dot(p[0], R)

    # calculate affine transformation matrix
    return np.column_stack((np.row_stack((R, t)),
                            (0, 0, 0, 1)))

对于您的示例输入，这将恢复与您从 CAD 程序获得的矩阵完全相同的矩阵：

>>> recover_homogenous_affine_transformation(
        np.array(((1.0,1.0,1.0),
                  (1.0,2.0,1.0),
                  (1.0,1.0,2.0))),
        np.array(((2.4142135623730940, 5.732050807568877, 0.7320508075688767),
                  (2.7677669529663684, 6.665063509461097, 0.6650635094610956),
                  (2.7677669529663675, 5.665063509461096, 1.6650635094610962))))
array([[ 0.8660254 , -0.35355339, -0.35355339,  0.        ],
       [ 0.35355339,  0.9330127 , -0.0669873 ,  0.        ],
       [ 0.35355339, -0.0669873 ,  0.9330127 ,  0.        ],
       [ 0.84108138,  5.21957879,  0.21957879,  1.        ]])