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point-cloud segmentation of individual trees in complex natural forest scenes based on a trunk-growth method

Journal of Forestry Research 2021年第6期32卷 2403-2414页

作者： Qianwei Liu Weifeng Ma Jianpeng Zhang Yicheng Liu Dongfan Xu Jinliang Wang Faculty of Geography Yunnan Normal UniversityKunming 650092YunnanPeople’s Republic of China Key Laboratory of Resources and Environmental Remote Sensing for Universities in Yunnan Kunming 650500YunnanPeople’s Republic of China Center for Geospatial Information Engineering and Technology of Yunnan Province Kunming 650500YunnanPeople’s Republic of China Forestry College Southwest Forestry UniversityKunming 650224YunnanPeople’s Republic of China

Forest resource management and ecological assessment have been recently supported by emerging *** laser scanning(TLS)is one that can be quickly and accurately used to obtain three-dimensional forest information,and create good representations of forest vertical *** data can be exploited for highly significant tasks,particularly the segmentation and information extraction for individual ***,the existing single-tree segmentation methods suffer from low segmentation accuracy and poor robustness,and hence do not lead to satisfactory results for natural forests in complex *** this paper,we propose a trunk-growth(TG)method for single-tree point-cloud segmentation,and apply this method to the natural forest scenes of Shangri-La City in Northwest Yunnan,***,the point normal vector and its Z-axis component are used as trunk-growth ***,the points surrounding the trunk are searched to account for ***,the nearest distributed branch and leaf points are used to complete the individual tree *** results show that the TG method can effectively segment individual trees with an average F-score of *** proposed method applies to many types of trees with various growth shapes,and can effectively identify shrubs and herbs in complex scenes of natural *** promising outcomes of the TG method demonstrate the key advantages of combining plant morphology theory and LiDAR technology for advancing and optimizing forestry systems.

关键词： Terrestrial laser scanning point-cloud Northwest Yunnan Natural forests Single-tree segmentation Trunk-growth

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Three Dimensional Metal-Surface Processing Parameter Generation Through Machine Learning-Based Nonlinear Mapping

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清华大学学报:自然科学版(英文版) 2023年第4期28卷 754-768页

作者： Min Zhu Yanjun Dong Bingqing Shen Haiyan Yu Lihong Jiang Hongming Cai School of Software Shanghai Jiao Tong UniversityShanghai 200240China School of Mechanical Engineering Donghua UniversityShanghai 200240China

The accuracy and efficiency of three-dimensional(3D)surface forming,which directly affects the cycle and quality of production,is important in *** practice,given the uncertainty of metal plate springback,an error exists between the actual plate and the target surface,which creates a nonlinear mapping from computer aided design models to bending *** need to reconfigure parameters and process a surface multiple times to delicately control springback,which greatly wastes human and material *** study aims to address the springback control problem to improve the efficiency and accuracy of sheet metal forming.A basic computation approach is proposed based on the DeepFit model to calculate the springback value in 3D surface *** address the sample data shortage problem,we put forward an advanced approach by combining a deep learning model with case-based reasoning(CBR).Next,a multi-model fused bending parameter generation framework is devised to implement the advanced springback computation approach through surface data preprocessing,CBR-based model matching,convolution neural network-based machining surface generation,and bending parameter generation with a series of model ***,the proposed approaches and the framework are verified by considering saddle surface processing as an ***,this study provides a new idea to improve the accuracy and efficiency of surface processing.

关键词： 3D surface point-cloud machine learning case-based reasoning industrial software

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Endowing rotation invariance for 3D finger shape and vein verification

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Frontiers of Computer Science 2022年第5期16卷 115-130页

作者： Hongbin XU Weili YANG Qiuxia WU Wenxiong KANG School of Software Engineering South China University of TechnologyGuangzhou 510006China School of Automation Science and Engineering South China University of TechnologyGuangzhou 510641China

Finger vein biometrics have been extensively studied for the capability to detect aliveness,and the high security as intrinsic ***,vein pattern distortion caused by finger rotation degrades the performance of CNN in 2D finger vein recognition,especially in a contactless *** address the finger posture variation problem,we propose a 3D finger vein verification system extracting axial rotation invariant *** efficient 3D finger vein reconstruction optimization model is proposed and several accelerating strategies are adopted to achieve real-time 3D reconstruction on an embedded *** main contribution in this paper is that we are the first to propose a novel 3D point-cloud-based endto-end neural network to extract deep axial rotation invariant feature,namely *** the network,the rotation problem is transformed to a permutation problem with the help of specially designed rotation ***,to validate the performance of the proposed network more rigorously and enrich the database resources for the finger vein recognition community,we built the largest publicly available 3D finger vein dataset with different degrees of finger rotation,namely the Large-scale Finger Multi-Biometric Database-3D Pose Varied Finger Vein(SCUT LFMB-3DPVFV)*** results on 3D finger vein datasets show that our 3DFVSNet holds strong robustness against axial rotation compared to other approaches.

关键词： 3D finger-vein biometrics point-cloud CNN

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