> return such blatant false matches long answer, have a try on this demo: https://docs.opencv.org/4.x/dc/dc3/tutorial_py_matcher.html https://docs.opencv.org/4

> return such blatant false matches long answer, have a try on this demo: https://docs.opencv.org/4.x/dc/dc3/tutorial_py_matcher.html https://docs.opencv.org/4.x/dc/dc3/tutorial_py_matcher.html short answer is that they are similar enough features to match. think of them as homophones (ie words that sound the same but have different meanings) in language. You need context to be able to filter them out. (https://github.com/polygon-software/python-visual-odometry/blob/master/Chapter%2010%20-%20RANSAC%20%26%20Bundle%20Adjustment.ipynb https://github.com/polygon-software/python-visual-odometry/b...) > don't fluctuate all that much. Over time that doesn't bear out. Good features are areas of high contrast with nice clearly defined edges (text is great, so are buildings). branches move, which means they create lots of diffrent features depending on the wind, even light wind. when we were building out maps, we filtered as much greenery out as possible