Optimal Randomized RANSAC

By Ondřej Chum, Jiri Matas. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008

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Back to the Feature: Learning Robust Camera Localization from Pixels to Pose (2021, methodology): “On the other hand, iii) is tackled by classical geometry using feature matching [19,20,28] or image alignment [4,26,27,51] and a 3D representation.”
Improving Image-Based Localization by Active Correspondence Search (2012, methodology): “After finding N =100 matches, the camera pose is estimated with a RANSAC-variant [22] using the 6-point DLT algorithm [16].”
MAGSAC: Marginalizing Sample Consensus (2018, methodology): “For this purpose, we apply SPRT [2] with a τref threshold.”
Fast image-based localization using direct 2D-to-3D matching (2011, methodology): “The 6-point DLT algorithm [10] is used in a RANSAC loop [4] to estimate the 3D pose of the camera.”
Performance Evaluation of RANSAC Family (2009, methodology): “Survey and performance evaluation including the recent works [7, 14, 26] contributes users to choose a proper method for their applications.”
Image Retrieval for Image-Based Localization Revisited (2012, methodology): “The camera pose of the query images is estimated from these matches using the 6-point DLT algorithm [12] inside a RANSAC loop [5, 6].”
Efficient & Effective Prioritized Matching for Large-Scale Image-Based Localization (2017, methodology): “We use the standard 6-point DLT (p6p) algorithm to compute the projection matrix from six 2D-3D matches [39] inside a SPRT-RANSAC [41] loop, followed by”