A Comparative Analysis of RANSAC Techniques Leading to Adaptive Real-Time Random Sample Consensus

By Rahul Raguram, Jan-Michael Frahm, Marc Pollefeys. European Conference on Computer Vision, 2008

580 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Artificial Intelligence, Media Technology

View Paper: https://www.semanticscholar.org/paper/72b8d4907191f0b86eb525a84eb5b1c6ce39d14b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

In Artificial Intelligence

In Media Technology

Citations in the corpus

Fast Global Registration (2016, methodology): “This iterative process is typically based on variants of RANSAC [1, 19, 26, 29, 34] or pose clustering [8, 26, 35].”
Learning accurate kinematic control of cable-driven surgical robots using data cleaning and Gaussian Process Regression (2014, methodology): “Details of our specific data collection procedure can be found in Section V.”
Building Rome on a Cloudless Day (2010, methodology): “Sample input images (courtesy of Flickr users: jdn, parris , Rictor Norton & David Allen, Felpa Boy, Andreas Solberg, xiquinhosilva, HarshLight,stevesheriw), local iconic scene graph and 3D model The merging step again uses ARRSAC [14] to robustly estimate a similarity transformation based on the id”
DSAC — Differentiable RANSAC for Camera Localization (2016, background): “RANSAC algorithm [11]. Most works focus on either or both of two aspects: speed [8,28,7], or quality of the ﬁnal estimate [39,8]. For detailed information about RANSAC variants we refer the reader to [30]. To the best of our knowledge, this work is the ﬁrst to introduce a differentiable variant of R”
GMS: Grid-Based Motion Statistics for Fast, Ultra-Robust Feature Correspondence (2017, unknown): “RANSAC [10, 41, 5, 32, 6, 36, 15] can leverage geometric information to alleviate this problem.”
Performance Evaluation of RANSAC Family (2009, methodology): “[22] recently performed evaluation for adaptive aspects of RANSAC.”
From structure-from-motion point clouds to fast location recognition (2009, background): “[17]) to determine the absolute pose from three point correspondences [5, 7].”
SuperGlue: Learning Feature Matching With Graph Neural Networks (2019, methodology): “The classical pipeline developed in the 2000s is often based on SIFT [28], filters matches with Lowe’s ratio test [28], the mutual check, and heuristics such as neighborhood consensus [53, 9, 5, 45], and finds a transformation with a robust solver like RANSAC [19, 40].”
On the segmentation of 3D LIDAR point clouds (2011, methodology): “4) Base-Of With Ground Method: Ground segmentation is performed and then the Base-Of method is applied to the remaining non-ground voxels.”
Visual simultaneous localization and mapping: a survey (2012, background): “A comparative analysis of these estimators can be found in (Raguram et al. 2008).”
Robust Point Matching via Vector Field Consensus (2014, methodology): “In this paper, we propose an efficient algorithm, called vector field consensus, for establishing robust point correspondences between two sets of points.”