c++ - OpenCV解决方案

我在Windows上使用OpenCV 3.1。

一段代码:

RNG rng; // random number generator

cv::Mat rVec = (cv::Mat_<double>(3, 1) << 0.08257, -0.6168, 1.4675);
cv::Mat tVec = (cv::Mat_<double>(3, 1) << -0.3806, -0.1605, 0.6087);

for (int i = 0; i < 10000; i++)
{
    rVec.ptr<double>(0)[0] += rng.rand_linear(0.0, 0.5); // mean 0, marin +-0.5
    rVec.ptr<double>(1)[0] += rng.rand_linear(0.0, 0.5);
    rVec.ptr<double>(2)[0] += rng.rand_linear(0.0, 0.5);
    tVec.ptr<double>(0)[0] += rng.rand_linear(0.0, 0.5);
    tVec.ptr<double>(1)[0] += rng.rand_linear(0.0, 0.5);
    tVec.ptr<double>(2)[0] += rng.rand_linear(0.0, 0.5);

    std::cout << rVec.t() << " --> ";
    bool success = cv::solvePnPRansac(patternPoints3d, imgPoints, camIntrinsics.camMat, camIntrinsics.distCoeffs, rVec, tVec, true, 100, 8.f, 0.99, cv::noArray(), cv::SOLVEPNP_ITERATIVE);
    std::cout << rVec.t() << std::endl;
}

输出类似于:

[-0.2853612945502569, -0.9418475404979531, 1.68440248184304] --> [0.0825705957511495, -0.6167564127260689, 1.467542801941067]
[0.1479919034434538, -0.2763278773652259, 1.150822641518221] --> [0.0825705957511495, -0.6167564127260689, 1.467542801941067]
[0.0706268803594689, -0.9919233518319074, 1.32315553697224] --> [0.0825705957511495, -0.6167564127260689, 1.467542801941067]
[0.3478958481835257, -0.3697621750777457, 1.716206426456824] --> [0.0825705957511495, -0.6167564127260689, 1.467542801941067]
[-0.3340069694997688, -0.3675019960516933, 1.51973527339685] --> [0.0825705957511495, -0.6167564127260689, 1.467542801941067]
[0.5445069792592954, -0.9075993847234044, 1.259690332649529] --> [0.0825705957511495, -0.6167564127260689, 1.467542801941067]

因此，无论起始假设如何，我都会得到完全相同的最终结果。

继续，我将迭代次数从100减少

bool success = cv::solvePnPRansac(patternPoints3d, imgPoints, camIntrinsics.camMat, camIntrinsics.distCoeffs, rVec, tVec, true, 100, 8.f, 0.99, cv::noArray(), cv::SOLVEPNP_ITERATIVE);

仅限 1次迭代

bool success = cv::solvePnPRansac(patternPoints3d, imgPoints, camIntrinsics.camMat, camIntrinsics.distCoeffs, rVec, tVec, true, 1, 8.f, 0.99, cv::noArray(), cv::SOLVEPNP_ITERATIVE);

结果相同:

[0.4316089014435242, -0.3745184350425247, 1.000847428296015] --> [0.0825705957511495, -0.6167564127260689, 1.467542801941067]
[-0.06206055466029242, -0.6728777329569552, 1.324249691752971] --> [0.0825705957511495, -0.6167564127260689, 1.467542801941067]
[-0.2321970797231366, -0.2713987283075098, 1.36880229898195] --> [0.0825705957511495, -0.6167564127260689, 1.467542801941067]
[-0.3178144781006445, -0.5075788347182665, 1.912844335384921] --> [0.0825705957511495, -0.6167564127260689, 1.467542801941067]

进一步，将置信度参数从 0.99更改为

bool success = cv::solvePnPRansac(patternPoints3d, imgPoints, camIntrinsics.camMat, camIntrinsics.distCoeffs, rVec, tVec, true, 1, 8.f, 0.99, cv::noArray(), cv::SOLVEPNP_ITERATIVE);

降至 0.01

bool success = cv::solvePnPRansac(patternPoints3d, imgPoints, camIntrinsics.camMat, camIntrinsics.distCoeffs, rVec, tVec, true, 1, 8.f, 0.01, cv::noArray(), cv::SOLVEPNP_ITERATIVE);

结果相同:

[-0.1541070262057652, -0.9795359918514136, 0.9881938066838982] --> [0.0825705957511495, -0.6167564127260689, 1.467542801941067]
[-0.09741225946638182, -0.2123314354700837, 1.35100669316414] --> [0.0825705957511495, -0.6167564127260689, 1.467542801941067]
[0.4136190534016173, -0.5970452204944435, 1.596524650886908] --> [0.0825705957511495, -0.6167564127260689, 1.467542801941067]
[-0.1438873709732612, -0.6913048753647003, 1.76558963228415] --> [0.0825705957511495, -0.6167564127260689, 1.467542801941067]

内部阈值参数相同。这些参数似乎没有任何区别。结果实际上看起来不错，我只是想更好地理解它。

因此，我的结论是，不管参数如何，solvePnPRansac()都会执行相同的操作。我究竟做错了什么？

最佳答案

不幸的是，当前的(OpenCV 3.2)solvePnPRansac()方法与文档不符:

这是SOLVEPNP_EPNP方法，当点数为MSS(最小样本集)步骤的>= 5(请参见here)时将使用

最终相机姿态估计未考虑useExtrinsicGuess(请参阅here)

如果根据文档的预期行为可以提高精度，我将尝试提交问题，甚至可以提出拉式请求(如果有时间的话)，否则必须更改文档。

不确定是否了解您的代码:

您会生成一个随机的照相机姿势rvec和tvec

，但是您似乎从未更新过2D图像点(需要使用projectPoints())？

您的电话是:

bool success = cv::solvePnPRansac(patternPoints3d, imgPoints, camIntrinsics.camMat, camIntrinsics.distCoeffs, rVec, tVec, true, 100, 8.f, 0.99, cv::noArray(), cv::SOLVEPNP_ITERATIVE);

。

当您查看doc时，将使用标记SOLVEPNP_ITERATIVE，这是一种估计照相机姿态的方法，该方法使用迭代优化方案，因此需要对解决方案进行初始估计。

提供useExtrinsicGuess = true时，它将在参数中直接使用rvec和tvec，否则将在内部调用另一种方法来获得rvec和tvec的初始估计。

关于c++ - OpenCV解决方案，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/44131349/