library: OpenGV pages: http://laurentkneip.github.io/opengv brief: OpenGV is a collection of computer vision methods for solving geometric vision problems. It contains absolute-pose, relative-pose, triangulation, and point-cloud alignment methods for the calibrated case. All problems can be solved with central or non-central cameras, and embedded into a random sample consensus or nonlinear optimization context. Matlab and Python interfaces are implemented as well. The link to the above pages also shows links to precompiled Matlab mex-libraries. Please consult the documentation for more information. author: Laurent Kneip, The Australian National University contact: kneip.laurent@gmail.com https://laurentkneip.github.io/opengv/page_installation.html git clone https://github.com/laurentkneip/opengv sudo apt-get install build-essential sudo apt-get install cmake sudo apt-get install cmake libeigen3-dev mkdir build && cd build && cmake .. && make 2-entity RANSAC: author: Yanmei Jiao, Zhejiang University contact: ymjiao@zju.edu.cn for monocular camera pose estimation: 1. 2P --> sac_problems::absolute_pose::AbsolutePoseSacProblem::TWOE2P 2. 1P1L --> sac_problems::absolute_pose::AbsoluteLinePoseSacProblem::TWOE1P1L 3. Mixed --> sac_problems::absolute_pose::AbsoluteLinePoseSacProblem::TWOEMixed for multiple camera pose estimaion: 1. MC2P --> sac_problems::absolute_pose::AbsolutePoseSacProblem::MC2P 2. MC1P1L --> sac_problems::absolute_pose::AbsoluteLinePoseSacProblem::MC1P1L 3. MCMixed --> sac_problems::absolute_pose::AbsoluteLinePoseSacProblem::MCMixed