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Binary Feature Descriptors, Performance?

My impression from reading a number of associated papers is that binary feature descriptors would be an order of magnitude faster (or more!) than for instance SIFT.

However, my results in a simple benchmark do not show this. All images + keypoints were pre-cached before running the benchmarks, so it's not that.

 DESCRIPTOR              MS        FEATURES          IMAGES      MS/FEATURE
      BRIEF             612          263120             230        0.002326
      BRISK             964          263120             230        0.003664
      FREAK             564          263120             230        0.002144
        ORB             476          247250             230        0.001925
       SIFT            1047          247250             230        0.004235

What's the reason for the poor results? Is it because of the implementations in OpenCV? Or am I missing something else?

Binary Feature Descriptors, Performance?

My impression from reading a number of associated papers is that binary feature descriptors would be an order of magnitude faster (or more!) than for instance SIFT.

However, my results in a simple benchmark do not show this. All images + keypoints were pre-cached before running the benchmarks, so it's not that.

 DESCRIPTOR              MS        FEATURES          IMAGES      MS/FEATURE
      BRIEF             612          263120             230        0.002326
      BRISK             964          263120             230        0.003664
      FREAK             564          263120             230        0.002144
        ORB             476          247250             230        0.001925
       SIFT            1047          247250             230        0.004235

What's the reason for the poor results? Is it because of the implementations in OpenCV? Or am I missing something else?

EDIT: For instance, in the FREAK-paper SIFT-description took 2.5 ms per key point vs 0.018 per key point for FREAK, that is, the latter is about 138 times faster to compute.