CPnP: Consistent Pose Estimator for Perspective-n-Point Problem with
Bias Elimination
- URL: http://arxiv.org/abs/2209.05824v1
- Date: Tue, 13 Sep 2022 09:00:58 GMT
- Title: CPnP: Consistent Pose Estimator for Perspective-n-Point Problem with
Bias Elimination
- Authors: Guangyang Zeng, Shiyu Chen, Biqiang Mu, Guodong Shi, and Junfeng Wu
- Abstract summary: We propose a consistent solver, named emphC, with bias elimination.
We analyze subtract bias based on which a closed-form least-squares solution is obtained.
Tests on both synthetic data and real images show that our proposed estimator is superior to some well-known ones for images with dense visual features.
- Score: 5.4234575866087384
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The Perspective-n-Point (PnP) problem has been widely studied in both
computer vision and photogrammetry societies. With the development of feature
extraction techniques, a large number of feature points might be available in a
single shot. It is promising to devise a consistent estimator, i.e., the
estimate can converge to the true camera pose as the number of points
increases. To this end, we propose a consistent PnP solver, named \emph{CPnP},
with bias elimination. Specifically, linear equations are constructed from the
original projection model via measurement model modification and variable
elimination, based on which a closed-form least-squares solution is obtained.
We then analyze and subtract the asymptotic bias of this solution, resulting in
a consistent estimate. Additionally, Gauss-Newton (GN) iterations are executed
to refine the consistent solution. Our proposed estimator is efficient in terms
of computations -- it has $O(n)$ computational complexity. Experimental tests
on both synthetic data and real images show that our proposed estimator is
superior to some well-known ones for images with dense visual features, in
terms of estimation precision and computing time.
Related papers
- Consistent and Asymptotically Statistically-Efficient Solution to Camera
Motion Estimation [11.69114446607907]
Given 2D point correspondences between an image pair, inferring the camera motion is a fundamental issue in the computer vision community.
We show that when the point number reaches the order of hundreds, our estimator outperforms the state-of-the-art ones in terms of estimation accuracy and CPU time.
arXiv Detail & Related papers (2024-03-02T10:56:27Z) - Closed-form Filtering for Non-linear Systems [83.91296397912218]
We propose a new class of filters based on Gaussian PSD Models, which offer several advantages in terms of density approximation and computational efficiency.
We show that filtering can be efficiently performed in closed form when transitions and observations are Gaussian PSD Models.
Our proposed estimator enjoys strong theoretical guarantees, with estimation error that depends on the quality of the approximation and is adaptive to the regularity of the transition probabilities.
arXiv Detail & Related papers (2024-02-15T08:51:49Z) - Learning Unnormalized Statistical Models via Compositional Optimization [73.30514599338407]
Noise-contrastive estimation(NCE) has been proposed by formulating the objective as the logistic loss of the real data and the artificial noise.
In this paper, we study it a direct approach for optimizing the negative log-likelihood of unnormalized models.
arXiv Detail & Related papers (2023-06-13T01:18:16Z) - Stochastic Inexact Augmented Lagrangian Method for Nonconvex Expectation
Constrained Optimization [88.0031283949404]
Many real-world problems have complicated non functional constraints and use a large number of data points.
Our proposed method outperforms an existing method with the previously best-known result.
arXiv Detail & Related papers (2022-12-19T14:48:54Z) - Beyond EM Algorithm on Over-specified Two-Component Location-Scale
Gaussian Mixtures [29.26015093627193]
We develop the Exponential Location Update (ELU) algorithm to efficiently explore the curvature of the negative log-likelihood functions.
We demonstrate that the ELU algorithm converges to the final statistical radius of the models after a logarithmic number of iterations.
arXiv Detail & Related papers (2022-05-23T06:49:55Z) - On Maximum-a-Posteriori estimation with Plug & Play priors and
stochastic gradient descent [13.168923974530307]
Methods to solve imaging problems usually combine an explicit data likelihood function with a prior that explicitly expected properties of the solution.
In a departure from explicit modelling, several recent works have proposed and studied the use of implicit priors defined by an image denoising algorithm.
arXiv Detail & Related papers (2022-01-16T20:50:08Z) - Heavy-tailed Streaming Statistical Estimation [58.70341336199497]
We consider the task of heavy-tailed statistical estimation given streaming $p$ samples.
We design a clipped gradient descent and provide an improved analysis under a more nuanced condition on the noise of gradients.
arXiv Detail & Related papers (2021-08-25T21:30:27Z) - Uncertainty-Aware Camera Pose Estimation from Points and Lines [101.03675842534415]
Perspective-n-Point-and-Line (Pn$PL) aims at fast, accurate and robust camera localizations with respect to a 3D model from 2D-3D feature coordinates.
arXiv Detail & Related papers (2021-07-08T15:19:36Z) - Bayesian imaging using Plug & Play priors: when Langevin meets Tweedie [13.476505672245603]
This paper develops theory, methods, and provably convergent algorithms for performing Bayesian inference with priors.
We introduce two algorithms: 1) -ULA (Unadjusted Langevin) Algorithm inference for Monte Carlo sampling and MMSE; and 2) quantitative-SGD (Stochastic Gradient Descent) for inference.
The algorithms are demonstrated on several problems such as image denoisering, inpainting, and denoising, where they are used for point estimation as well as for uncertainty visualisation and regularity.
arXiv Detail & Related papers (2021-03-08T12:46:53Z) - Relative Pose Estimation of Calibrated Cameras with Known
$\mathrm{SE}(3)$ Invariants [65.2314683780204]
We present a complete study of the relative pose estimation problem for a camera constrained by known $mathrmSE(3)$ invariants.
These problems reduces the minimal number of point pairs for relative pose estimation.
Experiments on synthetic and real data shows performance improvement compared to conventional relative pose estimation methods.
arXiv Detail & Related papers (2020-07-15T13:55:55Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.