7 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
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. The
proposed solvers use five 2D-2D image point correspondences induced by a scene
plane. One of them assumes the perspective camera to be fully calibrated, while
the other solver estimates the unknown focal length together with the absolute
pose parameters. This setup is particularly important in structure-from-motion
and image-based localization pipelines, where a new camera is localized in each
step with respect to a set of known cameras and 2D-3D correspondences might not
be available. As a consequence of a clever parametrization and the elimination
ideal method, our approach only needs to solve a univariate polynomial of
degree five or three. The proposed solvers are stable and efficient as
demonstrated by a number of synthetic and real-world experiments
Minimal Solutions for Relative Pose with a Single Affine Correspondence
In this paper we present four cases of minimal solutions for two-view relative pose estimation by exploiting the affine transformation between feature points and we demonstrate efficient solvers for these cases. It is shown, that under the planar motion assumption or with knowledge of a vertical direction, a single affine correspondence is sufficient to recover the relative camera pose. The four cases considered are two-view planar relative motion for calibrated cameras as a closed-form and a least-squares solution, a closedform solution for unknown focal length and the case of a known vertical direction. These algorithms can be used efficiently for outlier detection within a RANSAC loop and for initial motion estimation. All the methods are evaluated on both synthetic data and real-world datasets from the KITTI benchmark. The experimental results demonstrate that our methods outperform comparable state-of-the-art methods in
accuracy with the benefit of a reduced number of needed RANSAC iterations
Minimal Solutions for Relative Pose with a Single Affine Correspondence
In this paper we present four cases of minimal solutions for two-view
relative pose estimation by exploiting the affine transformation between
feature points and we demonstrate efficient solvers for these cases. It is
shown, that under the planar motion assumption or with knowledge of a vertical
direction, a single affine correspondence is sufficient to recover the relative
camera pose. The four cases considered are two-view planar relative motion for
calibrated cameras as a closed-form and a least-squares solution, a closed-form
solution for unknown focal length and the case of a known vertical direction.
These algorithms can be used efficiently for outlier detection within a RANSAC
loop and for initial motion estimation. All the methods are evaluated on both
synthetic data and real-world datasets from the KITTI benchmark. The
experimental results demonstrate that our methods outperform comparable
state-of-the-art methods in accuracy with the benefit of a reduced number of
needed RANSAC iterations.Comment: IEEE Conference on Computer Vision and Pattern Recognition (CVPR),
202
Affine Correspondences between Multi-Camera Systems for Relative Pose Estimation
We present a novel method to compute the relative pose of multi-camera
systems using two affine correspondences (ACs). Existing solutions to the
multi-camera relative pose estimation are either restricted to special cases of
motion, have too high computational complexity, or require too many point
correspondences (PCs). Thus, these solvers impede an efficient or accurate
relative pose estimation when applying RANSAC as a robust estimator. This paper
shows that the 6DOF relative pose estimation problem using ACs permits a
feasible minimal solution, when exploiting the geometric constraints between
ACs and multi-camera systems using a special parameterization. We present a
problem formulation based on two ACs that encompass two common types of ACs
across two views, i.e., inter-camera and intra-camera. Moreover, the framework
for generating the minimal solvers can be extended to solve various relative
pose estimation problems, e.g., 5DOF relative pose estimation with known
rotation angle prior. Experiments on both virtual and real multi-camera systems
prove that the proposed solvers are more efficient than the state-of-the-art
algorithms, while resulting in a better relative pose accuracy. Source code is
available at https://github.com/jizhaox/relpose-mcs-depth