AEP$n$P: A Less-constrained EP$n$P Solver for Pose Estimation with Anisotropic Scaling

• URL: http://arxiv.org/abs/2310.09982v4
• Date: Fri, 26 Jul 2024 12:52:19 GMT
• Title: AEP$n$P: A Less-constrained EP$n$P Solver for Pose Estimation with Anisotropic Scaling
• Authors: Jiaxin Wei, Stefan Leutenegger, Laurent Kneip,
• Abstract summary: We present a new approach to the P$nP problem with relaxed constraints, eliminating the need for precise 3D coordinates. Built upon the classical EPn$P solver, we refer to it as AEP$n$P due to its ability to handle unknown anisotropic scaling factors.
• Score: 26.396201197991118
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Perspective-$n$-Point (P$n$P) stands as a fundamental algorithm for pose estimation in various applications. In this paper, we present a new approach to the P$n$P problem with relaxed constraints, eliminating the need for precise 3D coordinates, which is especially suitable for object pose estimation where corresponding object models may not be available in practice. Built upon the classical EP$n$P solver, we refer to it as AEP$n$P due to its ability to handle unknown anisotropic scaling factors in addition to the common 6D transformation. Through a few algebraic manipulations and a well-chosen frame of reference, this new problem can be boiled down to a simple linear null-space problem followed by point registration-based identification of a similarity transformation. Experimental results on both simulated and real datasets demonstrate the effectiveness of AEP$n$P as a flexible and practical solution to object pose estimation. Code: https://github.com/goldoak/AEPnP.

Related papers

• DVMNet: Computing Relative Pose for Unseen Objects Beyond Hypotheses [59.51874686414509]
  Current approaches approximate the continuous pose representation with a large number of discrete pose hypotheses. We present a Deep Voxel Matching Network (DVMNet) that eliminates the need for pose hypotheses and computes the relative object pose in a single pass. Our method delivers more accurate relative pose estimates for novel objects at a lower computational cost compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-03-20T15:41:32Z)
• 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)
• CPPF++: Uncertainty-Aware Sim2Real Object Pose Estimation by Vote Aggregation [67.12857074801731]
  We introduce a novel method, CPPF++, designed for sim-to-real pose estimation. To address the challenge posed by vote collision, we propose a novel approach that involves modeling the voting uncertainty. We incorporate several innovative modules, including noisy pair filtering, online alignment optimization, and a feature ensemble.
  arXiv  Detail & Related papers  (2022-11-24T03:27:00Z)
• CPnP: Consistent Pose Estimator for Perspective-n-Point Problem with Bias Elimination [10.099598169569383]
  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.
  arXiv  Detail & Related papers  (2022-09-13T09:00:58Z)
• Perspective-1-Ellipsoid: Formulation, Analysis and Solutions of the Camera Pose Estimation Problem from One Ellipse-Ellipsoid Correspondence [1.7188280334580193]
  We introduce an ellipsoid-specific theoretical framework and demonstrate its beneficial properties in the context of pose estimation. We show that the proposed formalism enables to reduce the pose estimation problem to a position or orientation-only estimation problem.
  arXiv  Detail & Related papers  (2022-08-26T09:15:20Z)
• GPV-Pose: Category-level Object Pose Estimation via Geometry-guided Point-wise Voting [103.74918834553249]
  GPV-Pose is a novel framework for robust category-level pose estimation. It harnesses geometric insights to enhance the learning of category-level pose-sensitive features. It produces superior results to state-of-the-art competitors on common public benchmarks.
  arXiv  Detail & Related papers  (2022-03-15T13:58:50Z)
• 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)
• A scaling hypothesis for projected entangled-pair states [0.0]
  We introduce a new paradigm for scaling simulations with projected entangled-pair states (PEPS) for critical strongly-correlated systems. We use the effective correlation length $chi$ for inducing a collapse of data points, $f(D,chi)=f(xi(D,chi))$, for arbitrary values of $D$ and the environment bond dimension $chi$. We test our hypothesis on the critical 3-D dimer model, the 3-D classical Ising model, and the 2-D quantum Heisenberg model.
  arXiv  Detail & Related papers  (2021-02-05T12:48:01Z)
• 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.