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Found 28 papers, 12 papers with code
Relative Pose from SIFT Features
This paper proposes the geometric relationship of epipolar geometry and orientation- and scale-covariant, e. g., SIFT, features.
Deep MAGSAC++
We propose Deep MAGSAC++ combining the advantages of traditional and deep robust estimators.
Space-Partitioning RANSAC
A new algorithm is proposed to accelerate RANSAC model quality calculations.
VSAC: Efficient and Accurate Estimator for H and F
Experiments on four standard datasets show that VSAC is significantly faster than all its predecessors and runs on average in 1-2 ms, on a CPU.
USACv20: robust essential, fundamental and homography matrix estimation
We review the most recent RANSAC-like hypothesize-and-verify robust estimators.
Progressive-X+: Clustering in the Consensus Space
We propose Progressive-X+, a new algorithm for finding an unknown number of geometric models, e. g., homographies.
Calibrated and Partially Calibrated Semi-Generalized Homographies
In this paper, we propose the first minimal solutions for estimating the semi-generalized homography given a perspective and a generalized camera.
Globally Optimal Relative Pose Estimation with Gravity Prior
Smartphones, tablets and camera systems used, e. g., in cars and UAVs, are typically equipped with IMUs (inertial measurement units) that can measure the gravity vector accurately.
Minimal Solutions for Panoramic Stitching Given Gravity Prior
When capturing panoramas, people tend to align their cameras with the vertical axis, i. e., the direction of gravity.
Efficient Initial Pose-graph Generation for Global SfM
We propose ways to speed up the initial pose-graph generation for global Structure-from-Motion algorithms.
Pose Estimation for Vehicle-mounted Cameras via Horizontal and Vertical Planes
For the first class of solvers, the sought plane is expected to be perpendicular to one of the camera axes.
Minimal Cases for Computing the Generalized Relative Pose using Affine Correspondences
We propose three novel solvers for estimating the relative pose of a multi-camera system from affine correspondences (ACs).
Making Affine Correspondences Work in Camera Geometry Computation
The main advantage of such solvers is that their sample size is smaller, e. g., only two instead of four matches are required to estimate a homography.
Relative Pose from Deep Learned Depth and a Single Affine Correspondence
We propose a new approach for combining deep-learned non-metric monocular depth with affine correspondences (ACs) to estimate the relative pose of two calibrated cameras from a single correspondence.
EPOS: Estimating 6D Pose of Objects with Symmetries
A data-dependent number of corresponding 3D locations is selected per pixel, and poses of possibly multiple object instances are estimated using a robust and efficient variant of the PnP-RANSAC algorithm.
Least-squares Optimal Relative Planar Motion for Vehicle-mounted Cameras
A new closed-form solver is proposed minimizing the algebraic error optimally, in the least-squares sense, to estimate the relative planar motion of two calibrated cameras.
Relative planar motion for vehicle-mounted cameras from a single affine correspondence
A new minimal solver is proposed for the semi-calibrated case, i. e. the camera parameters are known except a common focal length.
MAGSAC++, a fast, reliable and accurate robust estimator
A new method for robust estimation, MAGSAC++, is proposed.
Homography from two orientation- and scale-covariant features
Two new general constraints are derived on the scales and rotations which can be used in any geometric model estimation tasks.
Progressive-X: Efficient, Anytime, Multi-Model Fitting Algorithm
The Progressive-X algorithm, Prog-X in short, is proposed for geometric multi-model fitting.
Progressive NAPSAC: sampling from gradually growing neighborhoods
We propose Progressive NAPSAC, P-NAPSAC in short, which merges the advantages of local and global sampling by drawing samples from gradually growing neighborhoods.
Optimal Multi-view Correction of Local Affine Frames
The technique requires the epipolar geometry to be pre-estimated between each image pair.
Recovering affine features from orientation- and scale-invariant ones
An approach is proposed for recovering affine correspondences (ACs) from orientation- and scale-invariant, e. g. SIFT, features.
MAGSAC: marginalizing sample consensus
A method called, sigma-consensus, is proposed to eliminate the need for a user-defined inlier-outlier threshold in RANSAC.
Five-point Fundamental Matrix Estimation for Uncalibrated Cameras
We aim at estimating the fundamental matrix in two views from five correspondences of rotation invariant features obtained by e. g.\ the SIFT detector.
A Minimal Solution for Two-view Focal-length Estimation using Two Affine Correspondences
To select the best one out of the remaining candidates, a root selection technique is proposed outperforming the recent ones especially in case of high-level noise.
Graph-Cut RANSAC
A novel method for robust estimation, called Graph-Cut RANSAC, GC-RANSAC in short, is introduced.
Multi-Class Model Fitting by Energy Minimization and Mode-Seeking
The move replaces a set of labels with the corresponding density mode in the model parameter domain, thus achieving fast and robust optimization.
