A small template object, a cluttered scene, and one question: is it in there, and where? Detect keypoints in both, fingerprint each one, match fingerprints, throw away the ones that lie, then let geometry itself draw a box around the answer.
Click any keypoint on the template. Its ORB binary fingerprint and SIFT gradient fingerprint are computed live, side by side, from the exact same patch of pixels.
ORB is a bit-string compared with cheap XOR+popcount — built for speed. SIFT-style is a normalized gradient histogram compared with Euclidean distance — the L2 normalisation makes it naturally tolerant of brightness and contrast changes ORB cannot see past.
Even good matching finds some wrong pairs by pure coincidence — two unrelated corners whose fingerprints happen to be close. Distance cutoff and the ratio test remove the obviously weak guesses.
Real matches from a real object all agree on one consistent transform (rotate, scale, shift). RANSAC repeatedly guesses a transform from a few matches and counts how many others agree with it — the largest agreeing group wins, everyone else is an outlier.
In Match & Verify, you turn RANSAC off entirely and set the ratio test very loose. What does the "web" of match lines look like?