QUICKSTART

This is the Author's implementation of BRISK: Binary Robust Invariant Scalable Keypoints [1]. Various (partly unpublished) extensions are provided, some of which are described in [2].

[1] Stefan Leutenegger, Margarita Chli and Roland Siegwart. BRISK: Binary Robust Invariant Scalable Keypoints, in Proceedings of the IEEE International Conference on Computer Vision (ICCV2011).

[2] Stefan Leutenegger. Unmanned Solar Airplanes: Design and Algorithms for Efficient and Robust Autonomous Operation. Doctoral dissertation, 2014.

Note that the codebase that you are provided here is free of charge and without any warranty.This is bleeding edge research software.

License

The 3-clause BSD license (see file LICENSE) applies.

How do I get set up?

Supported operating systems: Linux or MacOS X, tested on Ubuntu 14.04 and El Capitan. Vector instructions (SSE2 and SSSE3 or NEON) must be available.

Dependencies:

• OpenCV 2.4 or newer. OpenCV 3 is compatible, however not extensively tested and the demo application is somewhat limited in functionality.

Build instructions:

cd /path/to/brisk
mkdir build
cd build
cmake -DCMAKE_BUILD_TYPE=Release ..
make -j8

Run the demo application as

bin/demo

There are various options for the different versions of BRISK and other feature's detection and description. To see them, run

bin/demo --help

Using BRISK in your application

The demo.cc should give you enough details about how to use BRISK. We recommend the following setting:

• Detector: brisk::BriskFeatureDetector
• Descriptor: brisk::BriskDescriptorExtractor (brisk::BriskDescriptorExtractor::Version::briskV2)
• Matcher: brisk::BruteForceMatcher

Note about invariances: only use the ones you need specific to your application. Invarinaces will always reduce discriminative power.

For visual odometry/SLAM applications, we recommend a different detector that makes sure to distribute keypoints homogeneously in the image. Use brisk::HarrisScaleSpaceFeatureDetector(threshold, octaves, absoluteThreshold), where the threshold is inversely proportional to the feature density in the image and the absoluteThreshold should be set higher than 0 (e.g. 100), in order to suppress detections from noise in uniform areas.

Warning about the cv::KeyPoint size field: this is used to scale the BRISK sampling pattern. So when using non-brisk detectors, make sure this field is appropriately set. Too small values will lead to very bad descriptor performance; too large values, however, will lead to good descriptor discriminative power, but also to removal of many keypoints near the image border.

Contribution guidelines

If you would like to become a contributor, please contact s.leutenegger@imperial.ac.uk.

Requests, bug reports

Please read this guide carefully first! Contact s.leutenegger@imperial.ac.uk.