Unsupervised Learning of Consensus Maximization for 3D Vision Problems

Thomas Probst, Danda Pani Paudel, Ajad Chhatkuli & Luc Van Gool
Consensus maximization is a key strategy in 3D vision for robust geometric model estimation from measurements with outliers. Generic methods for consensus maximization, such as Random Sampling and Consensus (RANSAC), have played a tremendous role in the success of 3D vision, in spite of the ubiquity of outliers. However, replicating the same generic behaviour in a deeply learned architecture, using supervised approaches, has proven to be difficult. In that context, unsupervised methods have a huge...
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