Preemptive RANSAC for live structure and motion estimation

By David Nistér. Proceedings Ninth IEEE International Conference on Computer Vision, 2003

700 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/516606e4f245483f26dc400f5d7c128d858d5dd4

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

Visual odometry for ground vehicle applications (2006, methodology): “For all the camera motion estimation, we use preeemptive RANSAC as presented in (Nistér 2003b).”
A Comparative Analysis of RANSAC Techniques Leading to Adaptive Real-Time Random Sample Consensus (2008, methodology): “First, we provide a comparative analysis of the important RANSAC algorithms that have been proposed over the past few years [2,3,4,5,7,8,11].”
Get Out of My Lab: Large-scale, Real-Time Visual-Inertial Localization (2015, methodology): “These correspondences are then used to estimate the camera pose in a few milliseconds by applying a 3-point solver [14] inside a preemptive RANSAC [10] loop.”
Scene Coordinate Regression Forests for Camera Relocalization in RGB-D Images (2013, methodology): “To optimize this energy, we use an adapted version of preemptive RANSAC [24] (see Algorithm 1).”
Real-time markerless tracking for augmented reality: the virtual visual servoing framework (2006, background): “Another class of AR approaches, referred to as calibration-free approaches, uses affine or projective spaces for the integration of virtual objects [30], [48].”
Towards a benchmark for RGB-D SLAM evaluation (2011, background): “Different sensor modalities have been explored in the past, including 2D laser scanners [12], [3], 3D scanners [14], [16], monocular cameras [13], [7], [9], [19], [20] and stereo systems [8].”
Visual Odometry and Mapping for Autonomous Flight Using an RGB-D Camera (2011, methodology): “Inlier detection RANSAC based methods [28] are more commonly used than the greedy max-clique approach.”
Performance Evaluation of RANSAC Family (2009, methodology): “Preemptive RANSAC [21] performs parallel evaluation, while RANSAC do serial evaluation.”
3-D Mapping With an RGB-D Camera (2014, methodology): “Visual SLAM approaches [8]–[10], also referred to as " structure and motion estimation " [11], [12], compute the robot's motion and the map using cameras as sensors.”
Incremental smoothing and mapping (2008, methodology): “They are matched between successive frames using RANSAC [45] based on a trifocal camera-arrangement.”
DeepIM: Deep Iterative Matching for 6D Pose Estimation (2018, methodology): “By ﬁlter-ing out incorrect matches using robust estimation techniques such as RANSAC (Nist´er, 2005), the 6D pose of the object can be recovered using the 2D-to-3D correspondences between the local features.”
Two years of Visual Odometry on the Mars Exploration Rovers (2007, methodology): “This implementation contains two motion estimation schemes: the stereo scheme, which is an iterative pose refinement scheme, and the monocular scheme, which was based on the framework of 5-point algorithm (Nistér, 2004) and an outlier rejection scheme (Nistér, 2005) and good results have been repo”
Real-Time Monocular SLAM with Straight Lines (2006, unknown): “[15]), they do not guarantee repeated localisation.”
An Introduction to Inertial and Visual Sensing (2007, background): “Nistér presents a pre-emptive technique which gives high efficiency in limited time as required for real-time applications (Nistér 2005).”
Real-time monocular visual odometry for on-road vehicles with 1-point RANSAC (2009, methodology): “Using a single feature correspondence for motion estimation is the lowest model parameterization possible and results in the most efficient RANSAC algorithm.”
Vision-Based SLAM: Stereo and Monocular Approaches (2007, background): “Recent work by Nister (2003) show very nice re-sults.”