After I started using a mask with knnMatchAsync, I found that the result from knnMatchConvert would be clipped at random.

Investigating the issue, I found that knnMatchAsync will initialize all trainIdx to -1, which will be overwritten by the CUDA kernel. A mask can be used to prevent certain features from being matched and this will prevent the CUDA kernel from setting the match distance. knnMatchConvert is not properly incrementing the pointers when trainIdx == -1, so an unmatched feature will get it stuck at if (trainIdx == -1). Eventually the outer for-loop finishes and returns a vector with the matches up until the first missing match distance.

My solution is to increment the counters the same way as a succesful iteration would.

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