KFAST is the fastest CPU implementation of the FAST feature detector for computer vision, as described in the 2006 paper by Rosten and Drummond:

"Machine learning for high-speed corner detection" by Edward Rosten and Tom Drummond https://www.edwardrosten.com/work/rosten_2006_machine.pdf

Non-maximal suppression is supported. My implementation uses AVX2, multithreading, and many other careful optimizations to implement the FAST algorithm as described in the paper, but at great speed. This implementation outperforms the next-fastest implementation by 60% single-threaded or 500% multi-threaded (!) while exactly matching its output and capabilities.

The test driver requires OpenCV (for comparison and image load). KFAST itself requires AVX2 (for speeeeeeeeed!) but does NOT require OpenCV; it has no dependencies whatsoever.

Works on Linux (g++) and Windows (MSVC) with C++11 or higher.

Modern compiler and processor highly recommended.

On Linux you can do:

make
./KFAST

All functionality is contained within 'KFAST.h'. 'main.cpp' is a test driver demonstrating a performance comparison between Dr. Rosten's code, OpenCV's implementation, and my implementation. Dr. Rosten's implementation is contained in the folder 'Rosten'.

Simply plugging KFAST into ORB results in a several-fold speed improvement; however, there are a LOT of other things that need to be improved about ORB too, and I am working on it as time permits. I'll release them all at once when finished.

I would like to thank Dr. Rosten for his brilliant FAST and FASTER corner detectors. You can visit his website at www.edwardrosten.com or his GitHub at https://github.com/edrosten.

Dr. Rosten's work is BSD-licensed:

Copyright (c) 2006, 2008, 2009, 2010 Edward Rosten All rights reserved.

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

*Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.

*Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.

*Neither the name of the University of Cambridge nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

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