Multiview Point Cloud Registration Based on Minimum Potential Energy for Free-Form Blade Measurement

• URL: http://arxiv.org/abs/2502.07680v1
• Date: Tue, 11 Feb 2025 16:30:14 GMT
• Title: Multiview Point Cloud Registration Based on Minimum Potential Energy for Free-Form Blade Measurement
• Authors: Zijie Wu, Yaonan Wang, Yang Mo, Qing Zhu, He Xie, Haotian Wu, Mingtao Feng, Ajmal Mian,
• Abstract summary: We propose a novel global registration method that is based on the minimum potential energy (MPE) method to address these problems. The proposed algorithm outperforms the other global methods in terms of both accuracy and noise resistance.
• Score: 39.73816112194715
• License:
• Abstract: Point cloud registration is an essential step for free-form blade reconstruction in industrial measurement. Nonetheless, measuring defects of the 3D acquisition system unavoidably result in noisy and incomplete point cloud data, which renders efficient and accurate registration challenging. In this paper, we propose a novel global registration method that is based on the minimum potential energy (MPE) method to address these problems. The basic strategy is that the objective function is defined as the minimum potential energy optimization function of the physical registration system. The function distributes more weight to the majority of inlier points and less weight to the noise and outliers, which essentially reduces the influence of perturbations in the mathematical formulation. We decompose the solution into a globally optimal approximation procedure and a fine registration process with the trimmed iterative closest point algorithm to boost convergence. The approximation procedure consists of two main steps. First, according to the construction of the force traction operator, we can simply compute the position of the potential energy minimum. Second, to find the MPE point, we propose a new theory that employs two flags to observe the status of the registration procedure. We demonstrate the performance of the proposed algorithm on four types of blades. The proposed method outperforms the other global methods in terms of both accuracy and noise resistance.

Related papers

• Efficient Low-rank Identification via Accelerated Iteratively Reweighted Nuclear Norm Minimization [8.879403568685499]
  We introduce an adaptive updating strategy for smoothing parameters. This behavior transforms the algorithm into one that effectively solves problems after a few iterations. We prove the global proposed experiment, guaranteeing that every iteration is a critical one.
  arXiv  Detail & Related papers  (2024-06-22T02:37:13Z)
• SPARE: Symmetrized Point-to-Plane Distance for Robust Non-Rigid Registration [76.40993825836222]
  We propose SPARE, a novel formulation that utilizes a symmetrized point-to-plane distance for robust non-rigid registration. The proposed method greatly improves the accuracy of non-rigid registration problems and maintains relatively high solution efficiency.
  arXiv  Detail & Related papers  (2024-05-30T15:55:04Z)
• Partial Transport for Point-Cloud Registration [11.62232246430338]
  We propose a comprehensive set of non-rigid registration methods based on the optimal partial transportation problem. We extend our proposed algorithms via slicing to gain significant computational efficiency. We demonstrate the effectiveness of our proposed methods and compare them against baselines on various 3D and 2D non-rigid registration problems.
  arXiv  Detail & Related papers  (2023-09-27T17:04:22Z)
• GraphReg: Dynamical Point Cloud Registration with Geometry-aware Graph Signal Processing [0.0]
  This study presents a high-accuracy, efficient, and physically induced method for 3D point cloud registration. We explore geometry aware rigid-body dynamics to regulate the particle (point) motion, which results in more precise and robust registration. Results demonstrate that our proposed method outperforms state-of-the-art approaches in terms of accuracy and is more suitable for registering large-scale point clouds.
  arXiv  Detail & Related papers  (2023-02-02T14:06:46Z)
• Robust Motion Averaging for Multi-view Registration of Point Sets Based Maximum Correntropy Criterion [4.318555434063273]
  We propose a novel motion averaging framework for the multi-view registration with Laplacian kernel-based maximum correntropy criterion (LMCC) Our method achieves superior performance in terms of efficiency, accuracy and robustness.
  arXiv  Detail & Related papers  (2022-08-24T06:49:43Z)
• Graph Signal Restoration Using Nested Deep Algorithm Unrolling [85.53158261016331]
  Graph signal processing is a ubiquitous task in many applications such as sensor, social transportation brain networks, point cloud processing, and graph networks. We propose two restoration methods based on convexindependent deep ADMM (ADMM) parameters in the proposed restoration methods are trainable in an end-to-end manner.
  arXiv  Detail & Related papers  (2021-06-30T08:57:01Z)
• Canny-VO: Visual Odometry with RGB-D Cameras based on Geometric 3D-2D Edge Alignment [85.32080531133799]
  This paper reviews the classical problem of free-form curve registration and applies it to an efficient RGBD visual odometry system called Canny-VO. Two replacements for the distance transformation commonly used in edge registration are proposed: Approximate Nearest Neighbour Fields and Oriented Nearest Neighbour Fields. 3D2D edge alignment benefits from these alternative formulations in terms of both efficiency and accuracy.
  arXiv  Detail & Related papers  (2020-12-15T11:42:17Z)
• Adaptive Sampling for Best Policy Identification in Markov Decision Processes [79.4957965474334]
  We investigate the problem of best-policy identification in discounted Markov Decision (MDPs) when the learner has access to a generative model. The advantages of state-of-the-art algorithms are discussed and illustrated.
  arXiv  Detail & Related papers  (2020-09-28T15:22:24Z)
• Minimum Potential Energy of Point Cloud for Robust Global Registration [45.82423981744138]
  We propose a novel minimum gravitational potential energy (MPE)-based algorithm for global point set registration. We demonstrate the performance of the proposed algorithm on synthetic data as well as on real data.
  arXiv  Detail & Related papers  (2020-06-11T14:13:40Z)

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.