Fast Iterative Five point Relative Pose Estimation
- URL: http://arxiv.org/abs/2608.13114v1
- Date: Thu, 13 Aug 2026 11:41:29 GMT
- Title: Fast Iterative Five point Relative Pose Estimation
- Abstract summary: We propose a new iterative method, which is based on Powell's Dog Leg algorithm.<n>The new method has the same precision and is approximately twice as fast as Nister's algorithm.
- Score: 20.68512626541674
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Robust estimation of the relative pose between two cameras is a fundamental part of Structure and Motion methods. For calibrated cameras, the five point method together with a robust estimator such as RANSAC gives the best result in most cases. The current state-of-the-art method for solving the relative pose problem from five points is due to Nister [9], because it is faster than other methods and in the RANSAC scheme one can improve precision by increasing the number of iterations. In this paper, we propose a new iterative method, which is based on Powell's Dog Leg algorithm. The new method has the same precision and is approximately twice as fast as Nister's algorithm. The proposed method is easily extended to more than five points while retaining a efficient error metrics. This makes it also very suitable as an refinement step. The proposed algorithm is systematically evaluated on three types of datasets with known ground truth.
Related papers
- Practical solutions to the relative pose of three calibrated cameras [61.7981056628391]
We study the challenging problem of estimating the relative pose of three calibrated cameras from four point correspondences.<n>We propose novel efficient solutions to this problem that are based on the simple idea of using four correspondences to estimate an approximate geometry of the first two views.
arXiv Detail & Related papers (2023-03-28T15:50:48Z) - Efficient Second-Order Plane Adjustment [6.510507449705342]
This paper focuses on the problem of estimating the optimal planes and sensor poses to minimize the point-to-plane distance.
We adopt the Newton's method to efficiently solve the PA problem.
Empirical evaluation shows that our algorithm converges significantly faster than the widely used LM algorithm.
arXiv Detail & Related papers (2022-11-21T15:06:11Z) - Fast Projection onto the Capped Simplex withApplications to Sparse
Regression in Bioinformatics [6.6371396313325075]
We find that a simple algorithm based on Newton's method is able to solve the projection problem to high precision.
We demonstrate that the proposed algorithm can produce a solution of the projection problem with high precision on large scale datasets.
arXiv Detail & Related papers (2021-10-16T05:03:24Z) - Nesterov Accelerated ADMM for Fast Diffeomorphic Image Registration [63.15453821022452]
Recent developments in approaches based on deep learning have achieved sub-second runtimes for DiffIR.
We propose a simple iterative scheme that functionally composes intermediate non-stationary velocity fields.
We then propose a convex optimisation model that uses a regularisation term of arbitrary order to impose smoothness on these velocity fields.
arXiv Detail & Related papers (2021-09-26T19:56:45Z) - Generalized Pose-and-Scale Estimation using 4-Point Congruence
Constraints [5.063728016437489]
gP4Pc is a new method for computing the absolute pose of a generalized camera with unknown internal scale from four corresponding 3D point-and-ray pairs.
Our experiments on real and synthetic datasets, demonstrate that gP4Pc is among the fastest methods in terms of total running time when used within a SACRAN framework.
arXiv Detail & Related papers (2020-11-27T16:30:19Z) - Efficient Initial Pose-graph Generation for Global SfM [56.38930515826556]
We propose ways to speed up the initial pose-graph generation for global Structure-from-Motion algorithms.
The algorithms are tested on 402130 image pairs from the 1DSfM dataset.
arXiv Detail & Related papers (2020-11-24T09:32:03Z) - Robust Uncertainty-Aware Multiview Triangulation [20.02647320786556]
We propose a robust and efficient method for multiview triangulation and uncertainty estimation.
Our contribution is threefold: First, we propose an outlier rejection scheme using two-view RANSAC with the midpoint method.
Second, we compare different local optimization methods for refining the initial solution and the inlier set.
Third, we model the uncertainty of a triangulated point as a function of three factors: the number of cameras, the mean reprojection error and the maximum parallax angle.
arXiv Detail & Related papers (2020-08-04T00:47:42Z) - Aligning Partially Overlapping Point Sets: an Inner Approximation
Algorithm [80.15123031136564]
We propose a robust method to align point sets where there is no prior information about the value of the transformation.
Our algorithm does not need regularization on transformation, and thus can handle the situation where there is no prior information about the values of the transformations.
Experimental results demonstrate the better robustness of the proposed method over state-of-the-art algorithms.
arXiv Detail & Related papers (2020-07-05T15:23:33Z) - Accelerated Message Passing for Entropy-Regularized MAP Inference [89.15658822319928]
Maximum a posteriori (MAP) inference in discrete-valued random fields is a fundamental problem in machine learning.
Due to the difficulty of this problem, linear programming (LP) relaxations are commonly used to derive specialized message passing algorithms.
We present randomized methods for accelerating these algorithms by leveraging techniques that underlie classical accelerated gradient.
arXiv Detail & Related papers (2020-07-01T18:43:32Z) - Robust 6D Object Pose Estimation by Learning RGB-D Features [59.580366107770764]
We propose a novel discrete-continuous formulation for rotation regression to resolve this local-optimum problem.
We uniformly sample rotation anchors in SO(3), and predict a constrained deviation from each anchor to the target, as well as uncertainty scores for selecting the best prediction.
Experiments on two benchmarks: LINEMOD and YCB-Video, show that the proposed method outperforms state-of-the-art approaches.
arXiv Detail & Related papers (2020-02-29T06:24:55Z)
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.