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Accurately and robustly estimating three-dimensional rigid transformation using an effective local reference frame

OPTICAL ENGINEERING 2019年第7期58卷

作者： Zhao, Bao Chen, Xiaobo Le, Xinyi Xi, Juntong Shanghai Jiao Tong Univ State Key Lab Mech Syst & Vibrat Shanghai Peoples R China Shanghai Jiao Tong Univ Sch Mech Engn Shanghai Peoples R China Shanghai Jiao Tong Univ Shanghai Key Lab Adv Mfg Environm Shanghai Peoples R China

We present an accurate and efficient transformation estimation algorithm called local reference frame-based maximum consensus statistic (LRF-MCS). Moreover, a local reference frame (LRF) is proposed for effectively implementing our LRF-MCS. The proposed LRF uses a small local region to calculate its z axis and employs a salient local point to determine its x axis for improving the robustness to incomplete region. The proposed LRF-MCS first efficiently calculates the consensus set connected to each input correspondence by an LRF-based transformation technique. Then a switch program is used to select an appropriate consensus set for calculating the optimal transformation, which ensures the strong robustness to the seriously corrupted input correspondences. Extensive experiments are implemented on four standard datasets for comprehensively evaluating the performance of the proposed LRF and LRF-MCS techniques. The experimental results show that our LRF and LRF-MCS techniques outperform the state-of-the-art algorithms in terms of accuracy, efficiency, and robustness, achieving the best overall performance. (C) 2019 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： three-dimensional transformation estimation local reference frame three-dimensional registration

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Mathematical modeling of linearly-elastic non-prestrained cables based on a local reference frame

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APPLIED MATHEMATICAL MODELLING 2021年 91卷 695-708页

作者： Tang, H. B. Han, Y. Fu, H. Xu, B. G. Changan Univ Sch Automobile Xian Peoples R China Educ Univ Hong Kong Dept Math & Informat Technol Hong Kong Peoples R China Hong Kong Polytech Univ Inst Text & Clothing Hung Hom Kowloon Hong Kong Peoples R China

Cables are widely used and serve different purposes in engineering. This paper aims to formulate a general dynamic model on extensible non-prestrained cables under external forces. In terms of the Hamilton's principle, the governing equation and boundary conditions are achieved according to the variation of action integral. Meanwhile, the local reference frame of the cable curve is illustrated which is composed of four vectors. In the presented model, it is shown that the external force along the binormal direction could not be balanced by the internal tensile force of the cable itself. And the curved cable will result in an elastic force in the normal direction which is in a linear relationship with the curvature of the cable. Further, this approach is applied to cables under uniformly distributed loads or self-weights. The contours and internal tensile forces of the cables are figured out by means of numerical methods. The developed model is evaluated by means of experimental data in published literature. The good agreement between the numerical and experimental results shows that the presented method is feasible in theory. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Extensible non-prestrained cable The Hamilton's principle local reference frame Mathematical modeling

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A repeatable and robust local reference frame for 3D surface matching

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PATTERN RECOGNITION 2020年 100卷

作者： Ao, Sheng Guo, Yulan Tian, Jindong Tian, Yong Li, Dong Shenzhen Univ Coll Phys & Optoelect Engn Shenzhen 518060 Peoples R China Sun Yat Sen Univ Sch Elect & Commun Engn Guangzhou 510006 Peoples R China Natl Univ Def Technol Coll Elect Sci & Technol Changsha 410073 Peoples R China

local reference frames (LRFs) have been widely used for 3D local surface description. In this work, we propose a repeatable LRF with strong robustness to different nuisances. Different from existing LRF methods, the proposed LRF uses a part of neighboring points within the support region to calculate the z-axis, and performs an effective feature transformation on the neighboring points to define the x-axis. Specifically, feature transformation is applied to the data on a projection plane based on three point distribution characteristics via weighted strategies. These characteristics include the z-height, the distance to the center and the average length to 1-ring neighbors, covariance analysis is then applied to the transformed points to obtain the eigenvector with the largest eigenvalue, which points towards the maximum variance direction. Using a sign disambiguation technique, the modified eigenvector is used to define the final x-axis. Furthermore, a scale strategy is proposed to improve the robustness of the LRF with respect to mesh decimation. The proposed LRF was rigorously tested on six public benchmark datasets consisting of three different application contexts, i.e., 3D shape retrieval, 3D object recognition and registration. Experiments show that our method achieves significantly higher repeatability and stronger robustness than the state-of-the-arts under Gaussian noise, shot noise and mesh resolution variation. Finally, the descriptor matching results on four typical datasets further demonstrate the effectiveness of our LRF. (C) 2019 Published by Elsevier Ltd.

关键词： local reference frame 3D Surface description Feature transformation local characteristics Scale strategy

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A More Repeatable and Robust local reference frame for 3D local Surface Description 10

A More Repeatable and Robust Local Reference Frame for 3D Lo...

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10th International Conference on Graphics and Image Processing (ICGIP)

作者： Lu, Rongrong Zhu, Feng Wu, Qingxiao Fu, Xingyin Chinese Acad Sci Shenyang Inst Automat Shenyang 110016 Liaoning Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Key Lab Optoelect Informat Proc Shenyang 110016 Peoples R China Key Lab Image Understanding & Comp Vis Shenyang 110016 Liaoning Peoples R China

ISBN: (数字)9781510628298

ISBN: (纸本)9781510628298

Building 3D local surface feature with a local reference frame (LRF) can obtain rotational invariance and make use of 3D spatial information, thereby boosting the distinctiveness of a 3D local feature. However, this situation is based on the assumption that the LRF is stable and repeatable. Owing to the disturbances like noise, point density variation, occlusion and clutter, LRF may suffer ambiguity so that limit the ability of a LRF-based 3D local feature. This paper presents an efficient method for LRF construction. The experimental results show the superior performance of our proposed LRF in terms of repeatability and robustness on several popular datasets by comparing with the state-of-the-art methods. Moreover, our method is computational efficient as well.

关键词： 3D local surface feature local reference frame point cloud

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A local reference frame Construction Method Based on the Signed Surface Variation

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Journal of Geodesy and Geoinformation Science 2021年第3期4卷 25-37页

作者： Xiaopeng YAN Wei ZHOU Rencan PENG Wenliang PAN Lei WANG Guoxin HU The P.L.A.91550 Unit Dalian 116023China Department of Military Oceanography and Hydrography&Cartography Dalian Naval AcademyDalian 116018China

A fast local reference frame(LRF)construction method based on the signed surface variation is proposed,which can adapt to the real-time applications such as self-driving,face recognition,object *** z-axis of the LRF is generated based on the concavity of the local surface of *** x-axis is constructed by the weighted vector sum of a set of projection vectors of the local neighborhoods around *** performance of the proposed LRF is evaluated on six standard datasets and compared with six state-of-the-art LRF construction methods(e.g.,BOARD,FLARE,SHOT,RoPS and TOLDI).Experimental results validate the high repeatability,robustness,universality and time efficiency of our method.

关键词： signed surface variation local reference frame point cloud feature extraction

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SliceLRF: A local reference frame Sliced along the Height on the 3D Surface

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SENSORS 2023年第7期23卷 3483页

作者： Zhong, Bin Li, Dong Shenzhen Univ Coll Phys & Optoelect Engn Shenzhen 518000 Peoples R China Syst Educ Minist & Guangdong Prov Key Lab Optoelect Devices Shenzhen 518000 Peoples R China

The local reference frame (LRF) plays a vital role in local 3D shape description and matching. Numerous LRF methods have been proposed in recent decades. However, few LRFs can achieve a balance between repeatability and robustness under exposure to a variety of nuisances, including Gaussian noise, mesh resolution variation, clutter, and occlusion. Additionally, most LRFs are heuristic and lack generalizability to different applications and data modalities. In this paper, we first define the degree of distinction to describe the distribution of 2D point clouds and explore the relationship between the relative deviation of the distinction degree and the LRF error through experiments. Based on Gaussian noise and a random sampling analysis, several factors that affect the relative deviation of the distinction degree and result in the LRF error are identified. A scoring criterion is proposed to evaluate the robustness of the point cloud distribution. On this basis, we propose an LRF method (SliceLRF) based on slicing along the Z-axis, which selects the most robust adjacent slices in the point cloud region by scoring criteria for X-axis estimation to improve the repeatability and robustness. SliceLRF is rigorously tested on four public benchmark datasets which have different applications and involve different data modalities. It is also compared with the state-of-the-art LRFs. The experimental results show that the SliceLRF has more comprehensive repeatability and robustness than the other LRFs under exposure to Gaussian noise and random sampling.

关键词： local reference frame 3D local cloud descriptor distinction slices

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Describing local reference frames for 3-D Motion Trajectory Recognition

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IEEE ACCESS 2018年 6卷 36115-36121页

作者： Shao, Zhanpeng Li, Youfu Guo, Yao Zhou, Xiaolong Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou 310023 Zhejiang Peoples R China City Univ Hong Kong Dept Mech & Biomed Engn Hong Kong Hong Kong Peoples R China

Motion trajectories tracked from the points of interest can provide the key relevant features for characterizing the motion patterns in video. As the increasing number of 3-D vision sensors rises, the 3-D motion trajectories that serve as motion representations have been applied successfully to video retrieval and analysis, scene understanding, motion recognition, and so on, in existing works. Most of these works use raw data of motion trajectories directly or draw simple geometric quantities to describe the motion trajectories, whereas these simple descriptions are not intrinsically complete as they cannot feature the orientation changes of moving points along the 3-D motion trajectories. In principle, orientation changes of a single moving point in 3-D space have to been obtained by resorting to high-order derivatives, but the high-order derivatives would result in high sensitivity to noise. This paper tackles the problem by describing the local reference frames along 3-D motion trajectories, while we consider a motion trajectory as a temporal sequence of local reference frames. The maximal blurred segment of the noisy discrete curves is employed to estimate the local reference frames without high-order derivatives involved, and the local reference frame contains complete information of positions and orientations in the 3-D Euclidean space. To describe such local reference frames, we use the rotations and local square root velocities of local reference frames as the proposed descriptor to characterize the position and orientation changes of the moving points along the motion trajectories. In the experiments, we evaluate the effectiveness of the proposed descriptor by applying it to the gesture recognition on two large benchmark data sets that contain hand motion trajectories. The results show that our proposed descriptor can achieve superior performance compared to the existing descriptors and state-of-the-art methods in the 3 D motion trajectory recognition.

关键词： Motion trajectory gesture and activity recognition local reference frame maximal blurred segment

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Reframing Whole-Body Angular Momentum: Exploring the Impact of Low-Pass Filtered Dynamic local reference frames During Straight-Line and Turning Gaits

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2024年 32卷 3167-3178页

作者： Zhang, Junhao Veltink, Peter H. van Asseldonk, Edwin H. F. Univ Twente Dept Biomed Signals & Syst NL-7522 NB Enschede Netherlands Univ Twente Dept Biomech Engn Enschede Netherlands

Accurately estimating whole-body angular momentum (WBAM) during daily activities may benefit from choosing a locally-defined reference frame aligned with anatomical axes, particularly during activities involving body turns. local reference frames, potentially defined by pelvis heading angles, horizontal center of mass velocity (vCoM), or average angular velocity (A(omega)), can be utilized. To minimize the impact of inherent mediolateral oscillations of these frames, such as those caused by pelvis or vCoM rotation in the transverse plane, a low-pass filter is recommended. This study investigates how differences among global, local reference frames pre- and post-filtering affect WBAM component distribution across anatomical axes during straight-line walking and various turning tasks, which is lacking in the literature. Results highlighted significant effects of reference frame choice on WBAM distribution in the anteroposterior (AP) and mediolateral (ML) axes in all tasks. Specifically, expressing WBAM in the vCoM-oriented local reference frame yielded significantly lower (or higher) WBAM in the AP (or ML) axes compared to pelvis-oriented and A(omega)-oriented frames. However, these significant differences disappeared after employing a low-pass filter to local reference frames. Therefore, employing low-pass filtered local reference frames is crucial to enhance their applicability in both straight-line and turning tasks, ensuring more precise WBAM estimates. In applications that require expressing anatomical axes-dependent biomechanical parameters in a local reference frame, pelvis- and vCoM-oriented frames are more practical compared to the A(omega)-oriented frame, as they can be determined by a reduced optical marker set or inertial sensors in future applications when the whole-body kinematics is not available.

关键词： Whole-body angular momentum local reference frame turning gaits anatomical axes biomechanical measures Whole-body angular momentum local reference frame turning gaits anatomical axes biomechanical measures

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A local Discrete Feature Histogram for Point Cloud Feature Representation

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APPLIED SCIENCES-BASEL 2025年第5期15卷

作者： Jia, Linjing Li, Cong Xi, Guan Liu, Xuelian Xie, Da Wang, Chunyang Xian Technol Univ Xian Key Lab Act Photoelect Imaging Detect Technol Xian 710021 Peoples R China Xian Technol Univ Sch Optoelect Engn Xian 710021 Peoples R China

local feature descriptors are a critical problem in computer vision;the majority of current approaches find it difficult to achieve a balance between descriptiveness, robustness, compactness, and efficiency. This paper proposes the local discrete feature histogram (LDFH), a novel local feature descriptor, as a solution to this problem. The LDFH descriptor is constructed based on a robust local reference frame (LRF). It partitions the local space based on radial distance and calculates three geometric features, including the normal deviation angle, polar angle, and normal lateral angle, in each subspace. These features are then discretized to generate three feature statistical histograms, which are combined using a weighted fusion strategy to generate the final LDFH descriptor. Experiments on public datasets demonstrate that, compared with the existing methods, LDFH strikes an excellent balance between descriptiveness, robustness, compactness, and efficiency, making it suitable for various scenes and sensor datasets.

关键词： computer vision local feature descriptor point cloud local reference frame 3D representation

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LRF-Net: Learning local reference frames for 3D local Shape Description and Matching

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SENSORS 2020年第18期20卷 5086页

作者： Zhu, Angfan Yang, Jiaqi Zhao, Weiyue Cao, Zhiguo Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Natl Key Lab Sci & Technol Multispectral Informat Wuhan 430074 Peoples R China Northwestern Polytech Univ Sch Comp Sci Natl Engn Lab Integrated Aerosp Ground Ocean Big Xian 710129 Peoples R China

The local reference frame (LRF) acts as a critical role in 3D local shape description and matching. However, most existing LRFs are hand-crafted and suffer from limited repeatability and robustness. This paper presents the first attempt to learn an LRF via a Siamese network that needs weak supervision only. In particular, we argue that each neighboring point in the local surface gives a unique contribution to LRF construction and measure such contributions via learned weights. Extensive analysis and comparative experiments on three public datasets addressing different application scenarios have demonstrated that LRF-Net is more repeatable and robust than several state-of-the-art LRF methods (LRF-Net is only trained on one dataset). We show that LRFNet achieves 0.686 MeanCos performance on the UWA 3D modeling (UWA3M) dataset, outperforming the closest method by 0.18. In addition, LRF-Net can significantly boost the local shape description and 6-DoF pose estimation performance when matching 3D point clouds.

关键词： point cloud local reference frame deep learning

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