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目前我正在努力将可变形模型拟合到给定的图像（2d 图像到 3d 模型），但坚持使用拟合算法。
我已经阅读了几篇论文，大多数依赖于对我来说很难实现的优化技术，例如 SNO (blanz99,blanz03)、MFF 等。
有没有人实施过其中一种方法？你能分享一下代码吗？

还有两个问题：

  1. 如何计算参数的导数，尤其是渲染参数？
  2. 哪种拟合算法好且易于实现？
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Update...

If you're really just after some code - perhaps these specific face fitting/tracking libraries might be more what you are after (although I know they are more tending towards Active Appearance Models...)

I'm afraid that I don't have code for exactly what you're trying (it's been 7 years since I did any of this stuff I things have moved on somewhat. Good luck!)

...

Model issues aside - it sounds to me that you know what you are doing, and just need to express it in a form that you can use with any number of optimisation libraries out there...

I’m sure you probably know all this - but for this particular problem, short of asking the authors themselves for the code, or implementing it yourself, you are left with existing libraries. The issue then is often tailoring your model to work with them (hence my earlier questions).

e.g.

http://www.gnu.org/software/gsl/manual/html_node/Multimin-Algorithms.html

https://software.sandia.gov/opt++/

In turn used by many higher-level libraries, such as...

http://docs.scipy.org/doc/scipy/reference/optimize.html

http://stat.ethz.ch/R-manual/R-devel/library/stats/html/optim.html

http://weka.sourceforge.net/doc.dev/weka/core/Optimization.html

etc.

I think I can anticipate your core problem, which is that many of these ``off-the-shelf'' methods will not deliver the best fitting - since the number of parameters is pretty big and (as you hint at) are hard to factor.

As is so often the case with any form of model-fitting in Computer Vision, key advances are linked to fundamentally better optimisation (algorithm) and representation (data-structure/model).

So you could look at more experimental libraries, as opposed to the established ones above:

http://deeplearning.net/software/pylearn2/

http://ab-initio.mit.edu/wiki/index.php/NLopt

于 2014-07-02T12:04:09.237 回答