Please cite the following paper if using the library:
L. Kneip, P. Furgale, "OpenGV: A unified and generalized approach to real-time calibrated geometric vision", Proc. of The IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China. May 2014.
For more references, in particular about the algorithms that are included in OpenGV, please visit our research page on geometric camera pose estimation algorithms.
polyjam is a C++ library for setting up algebraic geometry problems and generating efficient C++ code that solves the underlying polynomial systems of equations. It notably does so by applying the theory of Groebner bases. polyjam is the driving force behind OpenGV, and all of MPL's geometric computer vision algorithms that involve the solution of multivariate polynomial equation systems contain solvers generated by this library. Problems of such form may be required in many engineering disciplines, which is why the tools provided through this library are of potentially broad applicability. Please read the documentation for in-depth user instructions.
Link: click here
Documentation: Instructions are now contained in the package
This is the original Matlab/C++ code for the P3P algorithm of Prof. Kneip. It is the state-of-the-art solution to the absolute pose problem, which consists of computing the position and orientation of a camera given 3 image-to-world-point correspondences. Execution time in C++ lies in the order of a microsecond on common machines. The algorithm requires normalized image points, and therefore requires the camera to be intrinsically calibrated. Note that the algorithm is also contained in OpenGV.
A modification of the BRIEF descriptor permitting an online rotation of the extraction pattern (useful if some knowledge of the orientation around the camera principle axis is given). The interface is OpenCV compatible, and extended by two further functions:
setRotationCase( double rotation ): sets a rotated pattern from the base pattern
freezeRotationCase(): transfers the rotated pattern to the base pattern
Documentation: See GitHub for tools usage (click here)
License: Creative Commons License & MIT
A set of synthesized realistic visual semantic SLAM dataset and benchmark with open source codes for customizing dataset. The indoor-scenario-oriented dataset includes RGBD, stereo, depth, semantic map, IMU, obejct model and location, groundtruth and hierarchical structure for both visual SLAM and neural network researches.