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SCM: Spatially Coherent Matching With Gaussian Field Learning for Nonrigid point Set Registration

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2021年第1期32卷 203-213页

作者： Wang, Gang Chen, Yufei Shanghai Univ Finance & Econ Inst Data Sci & Stat Sch Stat & Management Shanghai 200433 Peoples R China Tongji Univ Coll Elect & Informat Engn Shanghai 201804 Peoples R China

While point set registration has been studied in many areas of computer vision for decades, registering points encountering different degradations remains a challenging problem. In this article, we introduce a robust point pattern matching method, termed spatially coherent matching (SCM). The SCM algorithm consists of recovering correspondences and learning nonrigid transformations between the given model and scene point sets while preserving the local neighborhood structure. Precisely, the proposed SCM starts with the initial matches that are contaminated by degradations (e.g., deformation, noise, occlusion, rotation, multiview, and outliers), and the main task is to recover the underlying correspondences and learn the nonrigid transformation alternately. Based on unsupervised manifold learning, the challenging problem of point set registration can be formulated by the Gaussian fields criterion under a local preserving constraint, where the neighborhood structure could be preserved in each transforming. Moreover, the nonrigid transformation is modeled in a reproducing kernel Hilbert space, and we use a kernel approximation strategy to boost efficiency. Experimental results demonstrate that the proposed approach robustly rejecting mismatches and registers complex point set pairs containing large degradations.

关键词： Robustness Degradation Spatial coherence Strain Kernel Manifolds iterative closest point algorithm Correspondence modeling feature matching nonrigid registration robust point matching (RPM) spatially coherent matching (SCM)

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Inexact Bayesian point pattern matching for linear transformations

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PATTERN RECOGNITION 2014年第10期47卷 3265-3275页

作者： Christmas, J. Everson, R. M. Bell, J. Winlove, C. P. Univ Exeter Dept Comp Sci Exeter EX4 4QF Devon England Univ Exeter Peninsula Coll Med & Dent Exeter EX1 2LU Devon England Univ Exeter Dept Biophys Exeter EX4 4QF Devon England

We introduce a novel Bayesian inexact point pattern matching model that assumes that a linear transformation relates the two sets of points. The matching problem is inexact due to the lack of one-toone correspondence between the point sets and the presence of noise. The algorithm is itself inexact: we use variational Bayesian approximation to estimate the posterior distributions in the face of a problematic evidence term. The method turns out to be similar in structure to the iterative closest point algorithm. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Bayesian methods Variational approximation point pattern matching iterative closest point algorithm Linear transformation

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Fast and Robust iterative closest point

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2022年第7期44卷 3450-3466页

作者： Zhang, Juyong Yao, Yuxin Deng, Bailin Univ Sci & Technol China Sch Math Sci Hefei 230052 Anhui Peoples R China Cardiff Univ Sch Comp Sci & Informat Cardiff CF10 3AT Wales

The iterative closest point (ICP) algorithm and its variants are a fundamental technique for rigid registration between two point sets, with wide applications in different areas from robotics to 3D reconstruction. The main drawbacks for ICP are its slow convergence, as well as its sensitivity to outliers, missing data, and partial overlaps. Recent work such as Sparse ICP achieves robustness via sparsity optimization at the cost of computational speed. In this paper, we propose a new method for robust registration with fast convergence. First, we show that the classical point-to-point ICP can be treated as a majorization-minimization (MM) algorithm, and propose an Anderson acceleration approach to speed up its convergence. In addition, we introduce a robust error metric based on the Welsch's function, which is minimized efficiently using the MM algorithm with Anderson acceleration. On challenging datasets with noises and partial overlaps, we achieve similar or better accuracy than Sparse ICP while being at least an order of magnitude faster. Finally, we extend the robust formulation to point-to-plane ICP, and solve the resulting problem using a similar Anderson-accelerated MM strategy. Our robust ICP methods improve the registration accuracy on benchmark datasets while being competitive in computational time.

关键词： Convergence Acceleration Three-dimensional displays Measurement Optimization Robustness iterative closest point algorithm Rigid registration robust estimator fixed-point iterations majorlazer minimization method anderson acceleration

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Multilevel Optimization for Registration of Deformable point Clouds

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2020年 29卷 8735-8746页

作者： Chaudhury, Ayan INRIA Grenoble Rhone Alpes F-69364 Lyon France Ecole Normale Super Lyon F-69364 Lyon France

Handling deformation is one of the biggest challenges associated with point cloud registration. When deformation happens due to the motion of an animated object which actively changes its location and general shape, registration of two instances of the same object turns out to be a challenging task. The focus of this work is to address the problem by leveraging the complementary attributes of local and global geometric structures of the point clouds. We define an energy function which consists of local and global terms, as well as a semi-local term to model the intermediate level geometry of the point cloud. The local energy estimates the transformation parameters at the lowest level by assuming a reduced deformation model. The parameters are estimated in a closed form solution, which are then used to assign the initial probability of a stochastic model working at the intermediate level. The global energy term estimates the overall transformation parameters by minimizing a nonlinear least square function via Gauss-Newton optimization framework. The total energy is optimized in a block coordinate descent fashion, updating one term at a time while keeping others constant. Experiments on three publicly available datasets show that the method performs significantly better than several state-of-the-art algorithms in registering pairwise point cloud data.

关键词： Strain Three-dimensional displays Geometry Shape Deformable models Optimization iterative closest point algorithm Deformable registration quaternion gauss-newton nonlinear least squares Gaussian mixture model expectation maximization

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Comparison between point cloud processing techniques

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MEASUREMENT 2018年 127卷 221-226页

作者： Catalucci, Sofia Marsili, Roberto Moretti, Michele Rossi, Gianluca Univ Perugia Dipartimento Ingn Via Goffredo Duranti 93 I-06125 Perugia Italy Univ Nottingham Adv Mfg BldgJubilee Campus Nottingham NG8 1BB England

Photomodelling is an innovative, economical and fast technique based on the same principles of photogrammetry, which leads to the creation of 3-dimensional models starting from the simple acquisition of photographs. The aim of this paper is to define performances and metrological characteristics of this new technique and to understand the full potential offered by point cloud processing software. The analytical comparison considers a structured light 3D scanning system Creaform Go Scan 50 with metrological certification as a reference, in order to verify the accuracy and precision of photomodelling, using a modified function of the ICP algorithm and spatial, volumetric and superficial comparison criteria.

关键词： Photomodelling point cloud iterative closest point algorithm Measurement criteria

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Multientity Registration of point Clouds for Dynamic Objects on Complex Floating Platform Using Object Silhouettes

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2021年第1期59卷 769-783页

作者： Wang, Feng Hu, Han Ge, Xuming Xu, Bo Zhong, Ruofei Ding, Yulin Xie, Xiao Zhu, Qing Southwest Jiaotong Univ Fac Geosci & Environm Engn Chengdu 611756 Peoples R China Capital Normal Univ Beijing Adv Innovat Ctr Imaging Technol Beijing 100048 Peoples R China Zhejiang Hitarget Space Informat Technol Corp Ltd Huzhou 313200 Peoples R China

This article is focused on a challenging topic emerging from the registration of point clouds, specifically the registration of dynamic objects with low overlapping ratio. This problem is especially difficult when the static scanner is installed on a floating platform, and the objects it scans are also floating. These issues make most of the automatic registration methods and software solutions invalid. To solve this problem, explicit exploration of the static region is necessary for both the coarse and fine registration steps. Fortunately, determining the corresponding regions can be eased by the intuitive realization that in urban environments, natural objects neither present straight boundaries nor stack vertically. This intuition has guided the authors to develop a robust approach for the detection of static regions using planar structures. Then, silhouettes of the objects are extracted from the planar structures, which assist in the determination of an SE(2) transformation in the horizontal direction by a novel line matching method. The silhouettes also enable identification of the correspondences of planes in the step of fine registration using a variant of the iterative closest point method. Experimental evaluations using point clouds of cargo ships with different sizes and shapes reveal the robustness and efficiency of the proposed method, which gives 100 success and reasonable accuracy in rapid time, suitable for an online system. In addition, the proposed method is evaluated systematically with regard to several practical situations caused by the floating platform, and it demonstrates good robustness to limited scanning time and noise.

关键词： Three-dimensional displays Marine vehicles Robustness Nonhomogeneous media iterative closest point algorithm Databases Change detection line matching plane detection point clouds registration terrestrial laser scanning

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Registration with the point Cloud Library A Modular Framework for Aligning in 3-D

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IEEE ROBOTICS & AUTOMATION MAGAZINE 2015年第4期22卷 110-124页

作者： Holz, Dirk Ichim, Alexandru-Eugen Tombari, Federico Rusu, Radu B. Behnke, Sven Univ Bonn Bonn Germany Ecole Polytech Fed Lausanne Lausanne Switzerland Univ Bologna Bologna Italy Open Percept Inc Santa Margarita CA USA

Registration is an important step when processing three-dimensional (3-D) point clouds. Applications for registration range from object modeling and tracking, to simultaneous localization and mapping (SLAM). This article presents the open-source point cloud library (PCL) and the tools available for point cloud registration. The PCL incorporates methods for the initial alignment of point clouds using a variety of local shape feature descriptors, as well as methods for refining initial alignments using different variants of the well-known iterative closest point (ICP) algorithm. This article provides an overview on registration algorithms, usage examples of their PCL implementations, and tips for their application. Since the choice and parameterization of the right algorithm for a particular type of data is one of the biggest problems in 3-D point cloud registration, we present three complete examples of data (and applications) and the respective registration pipeline in the PCL. These examples include dense red-green-blue-depth (RGB-D) point clouds acquired by consumer color and depth cameras, high-resolution laser scans from commercial 3-D scanners, and low-resolution sparse point clouds captured by a custom lightweight 3-D scanner on a microaerial vehicle (MAV).

关键词： Three-dimensional displays Cloud computing iterative closest point algorithm Open source software iterative methods Robot sensing systems Sensors

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point Set Registration for 3D Range Scans Using Fuzzy Cluster-Based Metric and Efficient Global Optimization

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2021年第9期43卷 3229-3246页

作者： Liao, Qianfang Sun, Da Andreasson, Henrik Orebro Univ Ctr Appl Autonomous Sensor Syst AASS S-70281 Orebro Sweden

This study presents a new point set registration method to align 3D range scans. In our method, fuzzy clusters are utilized to represent a scan, and the registration of two given scans is realized by minimizing a fuzzy weighted sum of the distances between their fuzzy cluster centers. This fuzzy cluster-based metric has a broad basin of convergence and is robust to noise. Moreover, this metric provides analytic gradients, allowing standard gradient-based algorithms to be applied for optimization. Based on this metric, the outlier issues are addressed. In addition, for the first time in rigid point set registration, a registration quality assessment in the absence of ground truth is provided. Furthermore, given specified rotation and translation spaces, we derive the upper and lower bounds of the fuzzy cluster-based metric and develop a branch-and-bound (BnB)-based optimization scheme, which can globally minimize the metric regardless of the initialization. This optimization scheme is performed in an efficient coarse-to-fine fashion: First, fuzzy clustering is applied to describe each of the two given scans by a small number of fuzzy clusters. Then, a global search, which integrates BnB and gradient-based algorithms, is implemented to achieve a coarse alignment for the two scans. During the global search, the registration quality assessment offers a beneficial stop criterion to detect whether a good result is obtained. Afterwards, a relatively large number of points of the two scans are directly taken as the fuzzy cluster centers, and then, the coarse solution is refined to be an exact alignment using the gradient-based local convergence. Compared to existing counterparts, this optimization scheme makes a large improvement in terms of robustness and efficiency by virtue of the fuzzy cluster-based metric and the registration quality assessment. In the experiments, the registration results of several 3D range scan pairs demonstrate the accuracy and effectiveness of the

关键词： Measurement Optimization Three-dimensional displays iterative closest point algorithm Quality assessment Robustness Convergence point set registration fuzzy clusters registration quality assessment 3D range scans branch-and-bound

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Efficient scaling registration algorithm for partially overlapping point sets

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ELECTRONICS LETTERS 2013年第20期49卷 1267-1268页

作者： Ma, Liang Zhu, Jihua Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710049 Peoples R China

An efficient iterative closest point (ICP) variant is proposed for solving the scaling registration of partially overlapping point sets. By adopting the backward distance measurement and the overlapping rate, the afor... 详细信息

关键词： overlaps Dental Scaling SETTING Registration iterative closest point algorithm Inductively coupled plasma iterative Scale formation lap ratio distance measurement Scaling

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Bi-Direction ICP: Fast Registration Method of point Clouds 15

Bi-Direction ICP: Fast Registration Method of Point Clouds

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15th IAPR International Conference on Machine Vision Applications (MVA)

作者： Guan, Shiqiang Li, Guolin Xie, Xiang Wang, Zhihua Tsinghua Univ Inst Microelect Beijing Peoples R China Tsinghua Univ Dept Elect Engn Beijing Peoples R China

ISBN: (纸本)9784901122160

iterative closest point is a vital method in registration of point clouds. It runs slowly in case of large point clouds. In this article, we propose the Bi-Direction ICP in a 2D plane. The rotation model is used to complete Bi-Direction ICP in 3D space. Bi-Direction ICP decreases the iteration times by expanding each step angle width. This method concentrates on shortening the running time of ICP without losing the accuracy. Applied Bi-Direction ICP into Stanford 3D Scanning Repository and Mian's datasets, the registration running speed is 2.7 times at most of that in ICP. A subtle improvement occurs in the aspect of accuracy, compared to ICP.

关键词： iterative closest point algorithm Three-dimensional displays Bidirectional control Two dimensional displays Spinning Solid modeling Organizations

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