This week I implemented descriptors and method for matching points (based on euclidean distance and bruteforce comparison). Matching requires value of threshold. If threshold is low - number of coressponding points is small, but results are robust, and conversely. https://github.com/migal-drew/SimpleSURF_csharp
Next week I'm planning to port my code in GDAL, test algorithm and work on documentation.
Here is result in case if threshold is too high (false detections intersect other "true" matches)
Test with "sunflower field" image
And test with "standard" image in computer vision ("lenna image")