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Automatic Extraction Method of 3D Feature Guidelines for Complex Cultural Relic Surfaces Based on point cloud

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测绘学报（英文版） 2024年第1期7卷 16-41页

作者： GENG Yuxin ZHONG Ruofei HUANG Yuqin SUN Haili College of Resources Environment and Tourism Capital Normal UniversityBeijing 100048China Key Laboratory of 3-Dimensional Information Acquisition and Application Ministry of EducationBeijing 100048China Chinese Academy of Cultural Heritage Beijing 100029China

Cultural relics line graphic serves as a crucial form of traditional artifact information documentation,which is a simple and intuitive product with low cost of displaying compared with 3D *** reduction is undoubtedly necessary for line ***,most existing methods for artifact drawing rely on the principles of orthographic projection that always cannot avoid angle occlusion and data overlapping while the surface of cultural relics is ***,conformal mapping was introduced as a dimensionality reduction way to compensate for the limitation of orthographic *** on the given criteria for assessing surface complexity,this paper proposed a three-dimensional feature guideline extraction method for complex cultural relic surfaces.A 2D and 3D combined factor that measured the importance of points on describing surface fea-tures,vertex weight,was *** the selection threshold for feature guideline extraction was determined based on the differences between vertex weight and shape index *** feasibility and stability were verified through experiments conducted on real cultural relic surface *** demonstrated the ability of the method to address the challenges associated with the automatic generation of line drawings for complex *** extraction method and the obtained results will be useful for line graphic drawing,displaying and propaganda of cultural relics.

关键词： point cloud conformal parameterization vertex weight surface mesh cultural relics feature extraction

point cloud Based Semantic Segmentation Method for Unmanned Shuttle Bus

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Intelligent Automation & Soft Computing 2023年第9期37卷 2707-2726页

作者： Sidong Wu Cuiping Duan Bufan Ren Liuquan Ren Tao Jiang Jianying Yuan Jiajia Liu Dequan Guo School of Automation Chengdu University of Information TechnologyChengdu610225China

The complexity of application scenarios and the enormous volume of point cloud data make it difficult to quickly and effectively segment the scenario only based on the point *** this paper,to address the semantic segmentation for safety driving of unmanned shuttle buses,an accurate and effective point cloud-based semantic segmentation method is proposed for specified scenarios(such as campus).Firstly,we analyze the characteristic of the shuttle bus scenarios and propose to use ROI selection to reduce the total points in computation,and then propose an improved semantic segmentation model based on Cylinder3D,which improves mean Intersection over Union(mIoU)by 1.3%over the original model on SemanticKITTI data;then,a semantic category division method is proposed for road scenario of shuttle bus and practical application requirements,and then we further simplify the model to improve the efficiency without losing the ***,the nuScenes dataset and the real gathered campus scene data are used to validate and analyze the proposed *** experimental results on the nuScenes dataset and our data demonstrate that the proposed method performs better than other point cloud semantic segmentation methods in terms of application requirements for unmanned shuttle *** has a higher accuracy(82.73%in mIoU)and a higher computational efficiency(inference speed of 90 ms).

关键词： point cloud unmanned shuttle bus semantic segmentation

Development of a validation technique for road surface profile applicable to point cloud data

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Journal of Road Engineering 2022年第2期2卷 114-123页

作者： Kazuya Tomiyama Yuki Yamaguchi Kazushi Moriishi Yuki Kotani Division of Civil and Environmental Engineering Kitami Institute of TechnologyKitami 090-8507Japan Obayashi Road Corporation Tokyo 101-8228Japan Graduate School of Engineering Kitami Institute of TechnologyKitami 090-8507Japan

In recent years,various information and communication technology(ICT)devices measuring three-dimensional(3D)point cloud data have been developed and widely used for the application of pavement surface ***,ICT devices have generally been developed not only for measuring road surface profiles but for various geo-reference point *** this background,the validation of surface profiles acquired with ICT devices fulfils an important role in proving the reliability of measurement result composed by point *** study proposes a wavelet transform agreement(WTA)which involves a normalization factor of profile amplitude for further improvement in the wavelet-based coherence *** WTA analysis allows evaluating similarity/dissimilarity of two profiles considering both the location and wavelength *** this purpose,a terrestrial laser scanner(TLS),a mobile mapping system(MMS),and an unmanned aerial vehicle(UAV)are employed to prove the advantage of WTA in practical *** a result,the advantages of WTA analysis are clearly recognized in the optimization for the measurement interval of TLS,the multi-line measurement of MMS for ride quality improvement of a pavement,and the efficient operation of UAV in terms of the flight *** paper also shows the identification of aging development for surface roughness over time in terms of locations and *** findings help not only to guarantee the accuracy of profile measurements but to realize the sophisticated way of using 3D point clouds acquired with ICT *** outcomes of this study contribute to the increase of productivity for pavement works with improving the quality of surface profile measurement.

关键词： Surface profile Profile validation point cloud Wavelet agreement Wavelet coherence

Enhanced 3D point cloud Reconstruction for Light Field Microscopy Using U-Net-Based Convolutional Neural Networks

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Computer Systems Science & Engineering 2023年第12期47卷 2921-2937页

作者： Shariar Md Imtiaz Ki-Chul Kwon F.M.Fahmid Hossain MdBiddut Hossain Rupali Kiran Shinde Sang-Keun Gil Nam Kim Department of Information and Communication Engineering Chungbuk National UniversityCheongju-siChungcheongbuk-do28644Korea Department of Electronic Engineering University of SuwonHwaseong-siGyeonggi-do18323Korea

This article describes a novel approach for enhancing the three-dimensional(3D)point cloud reconstruction for light field microscopy(LFM)using U-net architecture-based fully convolutional neural network(CNN).Since the directional view of the LFM is limited,noise and artifacts make it difficult to reconstruct the exact shape of 3D point *** existing methods suffer from these problems due to the self-occlusion of the *** manuscript proposes a deep fusion learning(DL)method that combines a 3D CNN with a U-Net-based model as a feature *** sub-aperture images obtained from the light field microscopy are aligned to form a light field data cube for preprocessing.A multi-stream 3D CNNs and U-net architecture are applied to obtain the depth feature fromthe directional sub-aperture LF data *** the enhancement of the depthmap,dual iteration-based weighted median filtering(WMF)is used to reduce surface noise and enhance the accuracy of the *** a 3D point cloud involves combining two key elements:the enhanced depth map and the central view of the light field *** proposed method is validated using synthesized Heidelberg Collaboratory for Image Processing(HCI)and real-world LFM *** results are compared with different state-of-the-art *** structural similarity index(SSIM)gain for boxes,cotton,pillow,and pens are 0.9760,0.9806,0.9940,and 0.9907,***,the discrete entropy(DE)value for LFM depth maps exhibited better performance than other existing methods.

关键词： 3Dreconstruction 3Dmodeling point cloud depth estimation integral imaging light filedmicroscopy 3D-CNN U-Net deep learning machine intelligence

Two-Stage point cloud Super Resolution with Local Interpolation and Readjustment via Outer-Product Neural Network

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Journal of Systems Science & Complexity 2021年第1期34卷 68-82页

作者： WANG Guangyu XU Gang WU Qing WU Xundong School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou 310018China Key Laboratory of Complex Systems Modeling and Simulation Ministry of EducationHangzhou 310018China

This paper proposes a two-stage point cloud super resolution framework that combines local interpolation and deep neural network based readjustment. For the first stage, the authors apply a local interpolation method to increase the density and uniformity of the target point cloud. For the second stage, the authors employ an outer-product neural network to readjust the position of points that are inserted at the first stage. Comparison examples are given to demonstrate that the proposed framework achieves a better accuracy than existing state-of-art approaches, such as PU-Net, point Net and DGCNN(Source code is available at https://***/qwerty1319/PC-SR).

关键词： Neural network outer-product network point cloud super resolution

point cloud simplification algorithm based on particle swarm optimization for online measurement of stored bulk grain

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International Journal of Agricultural and Biological Engineering 2016年第1期9卷 71-78页

作者： Shao Qing Xu Tao Yoshino Tatsuo Zhao Yujie Yang Wenting Zhu Hang School of Mechanical Science and Engineering Jilin UniversityChangchun 130022China Jilin Academy of Agricultural Machinery Changchun 130022China

The simplification of 3D laser scanning point cloud is an important step of surface reconstruction and volume estimation of bulk grain in *** study presented an adaptive simplification algorithm based on particle swarm optimization(PSO).It introduced PSO into the average distance method,a conventional simplification *** basic idea of this algorithm was to adaptively determine the optimal point reducing intervals of scanning lines according to original point cloud density by *** using the 3D point cloud scanned from bulk grain surface in granary,the proposed algorithm was *** with the average distance method,the proposed algorithm obtained more evenly distributed point set,smaller reduction ratio(6.96%)and higher volume estimation accuracy(relative error was less than 3‰).The 3D laser scanner(GSLS003,Jilin University and SkyViTech Co.,Ltd.,Hangzhou,China)used in this study could scan the complete picture of the grain surface in a granary in one time,so the acquired point cloud data do not have to be *** the good simplification performance and capability of updating the reducing interval at any moment,the proposed algorithm and the 3D laser scanner could be used to realize online real-time measurement of stored bulk grain volume in granary.

关键词： point cloud simplification algorithm particle swarm optimization(PSO) 3D laser scanning large object stored grain

Stand parameter extraction based on video point cloud data

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Journal of Forestry Research 2021年第4期32卷 1553-1565页

作者： Ziyu Zhao Zhongke Feng Jincheng Liu Yudong Li Precision Forestry Key Laboratory of Beijing Beijing Forestry UniversityBeijing 100083People’s Republic of China College of Natural Resources and Environment Northwest A&F UniversityXian 712100People’s Republic of China

Monitoring sample plots is important for the sustainable management of forest *** resource data in the field is labor-intensive,time-consuming and *** the rapid development of hardware technology and photogrammetry,forest researchers have turned two-dimensional images into three-dimensional point clouds to obtain resource *** paper presents a method of sample plot analysis using two charge-coupled device(CCD) cameras based on video photography.A handheld CCD camera was used to shoot the sample plot by surrounding a central ***-based point clouds were used to detect and model individual tree trunks in the sample plots and the DBH of each was *** experimental results were compared with field measurement *** results show that the relative root mean squared error(rRMSE) of the DBH estimates of individual trees was 2.1-5.7%,acceptable for practical applications in traditional forest *** rRMSE of height estimates was2.7-36.3%.Average DBH and heights,and tree density and volume were ***-based methods require compact observation instruments,involve low costs during field investigations,acquire data with high efficiency,and point cloud data can be processed ***,this method can directly extract information on the relative position of trees,which is important to show distribution visually and provides a basis for researchers to regulate stand ***,video photography with its unique advantages is a technology warranting future attention for forest inventories and ecological construction.

关键词： Video photography point cloud Forest Hough transform DBH

3D point cloud Semantic Segmentation Based PAConv and SE_variant

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仪器仪表学报（英文版） 2023年第4期10卷 27-38页

作者： ZHANG Ying SUN Yue WU Lin ZHANG Lulu MENG Bumin School of automation and electronic information Xiangtan UniversityXiangtan 411105China

With the increasing popularity of 3D sensors(e.g.,Kinect)and light field cameras,technologies such as driverless,smart home and virtual reality have become hot spots for engineering *** an important part of 3D vision tasks,point cloud semantic segmentation has received a lot of attention from *** this work,we focus on realistically collected indoor point cloud data and propose a point cloud semantic segmenta-tion method based on PAConv and SE_*** SE_variant module captures global perception from a broad perspective of feature space by fusing different pooling methods,which fully utilize the channel information of point *** effectiveness of the method is verified by comparing with other methods on S3DIS and ScanNetV2 semantic tagging benchmarks,and achieving 65.3％mIoU in S3DIS,47.6％mIoU in *** results of the ablation experiments verify the effectiveness of the key modules and analyze how to use the attention mechanism to improve the 3D semantic segmentation performance.

关键词： Semantic Segmentation point cloud SE_variant Attention Mechanism

CONVERGENCE OF THE WEIGHTED NONLOCAL LAPLACIAN ON RANDOM point cloud

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Journal of Computational Mathematics 2021年第6期39卷 865-879页

作者： Zuoqiang Shi Bao Wang Department of Mathematical Sciences Tsinghua UniversityBeijing 100084China Department of Mathematics University of UtahSalt Lake CityUT 84112USA

We analyze the convergence of the weighted nonlocal Laplacian(WNLL)on the high dimensional randomly distributed point *** analysis reveals the importance of the scaling weight,µ∼|P|/|S|with|P|and|S|being the number of entire and labeled data,respectively,in *** established result gives a theoretical foundation of the WNLL for high dimensional data interpolation.

关键词： Weighted nonlocal Laplacian Laplace-Beltrami operator point cloud Highdimensional interpolation