32 research outputs found
Homography from two orientation- and scale-covariant features
This paper proposes a geometric interpretation of the angles and scales which
the orientation- and scale-covariant feature detectors, e.g. SIFT, provide. Two
new general constraints are derived on the scales and rotations which can be
used in any geometric model estimation tasks. Using these formulas, two new
constraints on homography estimation are introduced. Exploiting the derived
equations, a solver for estimating the homography from the minimal number of
two correspondences is proposed. Also, it is shown how the normalization of the
point correspondences affects the rotation and scale parameters, thus achieving
numerically stable results. Due to requiring merely two feature pairs, robust
estimators, e.g. RANSAC, do significantly fewer iterations than by using the
four-point algorithm. When using covariant features, e.g. SIFT, the information
about the scale and orientation is given at no cost. The proposed homography
estimation method is tested in a synthetic environment and on publicly
available real-world datasets
Rectification from Radially-Distorted Scales
This paper introduces the first minimal solvers that jointly estimate lens
distortion and affine rectification from repetitions of rigidly transformed
coplanar local features. The proposed solvers incorporate lens distortion into
the camera model and extend accurate rectification to wide-angle images that
contain nearly any type of coplanar repeated content. We demonstrate a
principled approach to generating stable minimal solvers by the Grobner basis
method, which is accomplished by sampling feasible monomial bases to maximize
numerical stability. Synthetic and real-image experiments confirm that the
solvers give accurate rectifications from noisy measurements when used in a
RANSAC-based estimator. The proposed solvers demonstrate superior robustness to
noise compared to the state-of-the-art. The solvers work on scenes without
straight lines and, in general, relax the strong assumptions on scene content
made by the state-of-the-art. Accurate rectifications on imagery that was taken
with narrow focal length to near fish-eye lenses demonstrate the wide
applicability of the proposed method. The method is fully automated, and the
code is publicly available at https://github.com/prittjam/repeats.Comment: pre-prin
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. Using this additional information, the -axes of
the cameras can be aligned, reducing their relative orientation to a single
degree-of-freedom. With this assumption, we propose a novel globally optimal
solver, minimizing the algebraic error in the least-squares sense, to estimate
the relative pose in the over-determined case. Based on the epipolar
constraint, we convert the optimization problem into solving two polynomials
with only two unknowns. Also, a fast solver is proposed using the first-order
approximation of the rotation. The proposed solvers are compared with the
state-of-the-art ones on four real-world datasets with approx. 50000 image
pairs in total. Moreover, we collected a dataset, by a smartphone, consisting
of 10933 image pairs, gravity directions, and ground truth 3D reconstructions
Hybrid Focal Stereo Networks for Pattern Analysis in Homogeneous Scenes
In this paper we address the problem of multiple camera calibration in the
presence of a homogeneous scene, and without the possibility of employing
calibration object based methods. The proposed solution exploits salient
features present in a larger field of view, but instead of employing active
vision we replace the cameras with stereo rigs featuring a long focal analysis
camera, as well as a short focal registration camera. Thus, we are able to
propose an accurate solution which does not require intrinsic variation models
as in the case of zooming cameras. Moreover, the availability of the two views
simultaneously in each rig allows for pose re-estimation between rigs as often
as necessary. The algorithm has been successfully validated in an indoor
setting, as well as on a difficult scene featuring a highly dense pilgrim crowd
in Makkah.Comment: 13 pages, 6 figures, submitted to Machine Vision and Application
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. Considering the depth
information and affine features, two new constraints on the camera pose are
derived. The proposed solver is usable within 1-point RANSAC approaches. Thus,
the processing time of the robust estimation is linear in the number of
correspondences and, therefore, orders of magnitude faster than by using
traditional approaches. The proposed 1AC+D solver is tested both on synthetic
data and on 110395 publicly available real image pairs where we used an
off-the-shelf monocular depth network to provide up-to-scale depth per pixel.
The proposed 1AC+D leads to similar accuracy as traditional approaches while
being significantly faster. When solving large-scale problems, e.g., pose-graph
initialization for Structure-from-Motion (SfM) pipelines, the overhead of
obtaining ACs and monocular depth is negligible compared to the speed-up gained
in the pairwise geometric verification, i.e., relative pose estimation. This is
demonstrated on scenes from the 1DSfM dataset using a state-of-the-art global
SfM algorithm. Source code: https://github.com/eivan/one-ac-pos
Unknown Radial Distortion Centers in Multiple View Geometry Problems
The radial undistortion model proposed by Fitzgibbon and the radial fundamental matrix were early steps to extend classical epipolar geometry to distorted cameras. Later minimal solvers have been proposed to find relative pose and radial distortion, given point correspondences between images. However, a big drawback of all these approaches is that they require the distortion center to be exactly known. In this paper we show how the distortion center can be absorbed into a new radial fundamental matrix. This new formulation is much more practical in reality as it allows also digital zoom, cropped images and camera-lens systems where the distortion center does not exactly coincide with the image center. In particular we start from the setting where only one of the two images contains radial distortion, analyze the structure of the particular radial fundamental matrix and show that the technique also generalizes to other linear multi-view relationships like trifocal tensor and homography. For the new radial fundamental matrix we propose different estimation algorithms from 9,10 and 11 points. We show how to extract the epipoles and prove the practical applicability on several epipolar geometry image pairs with strong distortion that - to the best of our knowledge - no other existing algorithm can handle properly
A sparse resultant based method for efficient minimal solvers
Abstract
Many computer vision applications require robust and efficient estimation of camera geometry. The robust estimation is usually based on solving camera geometry problems from a minimal number of input data measurements, i.e. solving minimal problems in a RANSAC framework. Minimal problems often result in complex systems of polynomial equations. Many state-of-the-art efficient polynomial solvers to these problems are based on Gröbner basis and the action-matrix method that has been automatized and highly optimized in recent years. In this paper we study an alternative algebraic method for solving systems of polynomial equations, i.e., the sparse resultant-based method and propose a novel approach to convert the resultant constraint to an eigenvalue problem. This technique can significantly improve the efficiency and stability of existing resultant-based solvers. We applied our new resultant-based method to a large variety of computer vision problems and show that for most of the considered problems, the new method leads to solvers that are the same size as the the best available Gröbner basis solvers and of similar accuracy. For some problems the new sparse-resultant based method leads to even smaller and more stable solvers than the state-of-the-art Gröbner basis solvers. Our new method can be fully automatized and incorporated into existing tools for automatic generation of efficient polynomial solvers and as such it represents a competitive alternative to popular Gröbner basis methods for minimal problems in computer vision