Separable Four Points Fundamental Matrix

We present a novel approach for RANSAC-based computation of the fundamental matrix based on epipolar homography decomposition. We analyze the geometrical meaning of the decomposition-based representation and show that it directly induces a consecutive sampling strategy of two independent sets of correspondences. We show that our method guarantees a minimal number of evaluated hypotheses with respect to current minimal approaches, on the condition that there are four correspondences on an image line. We validate our approach on real-world image pairs, providing fast and accurate results.

• Publication year

  2020

• Venue

  IEEE Workshop/Winter Conference on Applications of Computer Vision

• Publication date

  2020-06-10

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.1109/WACV48630.2021.00023arXiv 2006.05926
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Estimating the Fundamental Matrix Without Point Correspondences With Application to Transmission Imaging

  2019cited by this paper
• MAGSAC: Marginalizing Sample Consensus

  2018cited by this paper
• Five-Point Fundamental Matrix Estimation for Uncalibrated Cameras

  2018influential reference
• An Epipolar Line from a Single Pixel

  2017cited by this paper
• Graph-Cut RANSAC

  2017influential reference
• Epipolar geometry based on line similarity

  2016cited by this paper
• Structure-from-Motion Revisited

  2016influential reference
• Fundamental Matrices from Moving Objects Using Line Motion Barcodes

  2016cited by this paper
• Camera Calibration from Dynamic Silhouettes Using Motion Barcodes

  2015cited by this paper
• Robust Global Translations with 1DSfM

  2014cited by this paper
• Dense Segmentation-Aware Descriptors

  2013cited by this paper
• Distinctive Image Features from Scale-Invariant Keypoints Abstract by Matthijs Dorst Based on the paper by

  2011influential reference
• DAISY: An Efficient Dense Descriptor Applied to Wide-Baseline Stereo

  2010cited by this paper
• 3D Camera Pose Estimation Using Line Correspondences and 1D Homographies

  2010cited by this paper
• Camera Network Calibration and Synchronization from Silhouettes in Archived Video

  2010cited by this paper
• On benchmarking camera calibration and multi-view stereo for high resolution imagery

  2008cited by this paper
• Matching with PROSAC - progressive sample consensus

  2005cited by this paper
• A minimal solution for relative pose with unknown focal length

  2005cited by this paper
• Distinctive Image Features from Scale-Invariant Keypoints

  2004cited by this paper
• An efficient solution to the five-point relative pose problem

  2004cited by this paper
• The fundamental matrix: Theory, algorithms, and stability analysis

  2004cited by this paper
• Preemptive RANSAC for Live Structure and Motion Estimation

  2003cited by this paper
• Preemptive RANSAC for live structure and motion estimation

  2003cited by this paper
• The geometry of multiple images - the laws that govern the formation of multiple images of a scene and some of their applications

  2001cited by this paper
• Multiple View Geometry in Computer Vision

  2001influential reference
• In Defense of the Eight-Point Algorithm

  1997cited by this paper
• Six-point synthetic method to estimate fundamental matrix

  1997cited by this paper
• Readings in computer vision: issues, problems, principles, and paradigms

  1987cited by this paper
• Least Median of Squares Regression

  1984cited by this paper
• A computer algorithm for reconstructing a scene from two projections

  1981cited by this paper
• Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography

  1981cited by this paper
• Use of the Hough transformation to detect lines and curves in pictures

  1972cited by this paper

• No citing papers are available for this paper.