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CurveNet:Curvature-Based Multitask Learning deep Networks for 3d Object Recognition

IEEE/CAA Journal of Automatica Sinica 2021年第6期8卷 1177-1187页

作者： A.A.M.Muzahid Wanggen Wan Ferdous Sohel Lianyao Wu Li Hou School of Communications and Information Engineering Institute of Smart CityShanghai UniversityShanghai 200444China discipline of Information Technology Murdoch UniversityMurdoch WA 6150Australia School of Information Engineering Huangshan UniversityHuangshan 245041China

In computer vision fields,3d object recognition is one of the most important tasks for many real-world ***-dimensional convolutional neural networks(CNNs)have demonstrated their advantages in 3d object *** this paper,we propose to use the principal curvature directions of 3d objects(using a CAd model)to represent the geometric features as inputs for the 3d *** framework,namely CurveNet,learns perceptually relevant salient features and predicts object class *** directions incorporate complex surface information of a 3d object,which helps our framework to produce more precise and discriminative features for object *** learning is inspired by sharing features between two related tasks,where we consider pose classification as an auxiliary task to enable our CurveNet to better generalize object label *** results show that our proposed framework using curvature vectors performs better than voxels as an input for 3d object *** further improved the performance of CurveNet by combining two networks with both curvature direction and voxels of a 3d object as the inputs.A Cross-Stitch module was adopted to learn effective shared features across multiple *** evaluated our methods using three publicly available datasets and achieved competitive performance in the 3d object recognition task.

关键词： 3d shape analysis convolutional neural network dNNs object classification volumetric CNN

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Aggregate Point Cloud Geometric Features for Processing

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Computer Modeling in Engineering & Sciences 2023年第7期136卷 555-571页

作者： Yinghao Li Renbo Xia Jibin Zhao Yueling Chen Liming Tao Hangbo Zou Tao Zhang Shenyang Institute of Automation Chinese Academy of SciencesShenyang110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang110169China University of Chinese Academy of Sciences Beijing100049China

As 3d acquisition technology develops and 3d sensors become increasingly affordable,large quantities of 3d point cloud data are *** to effectively learn and extract the geometric features from these point clouds has become an urgent problem to be *** point cloud geometric information is hidden in disordered,unstructured points,making point cloud analysis a very challenging *** address this problem,we propose a novel network framework,called Tree Graph Network(TGNet),which can sample,group,and aggregate local geometric ***,we construct a Tree Graph by explicit rules,which consists of curves extending in all directions in point cloud feature space,and then aggregate the features of the graph through a cross-attention *** this way,we incorporate more point cloud geometric structure information into the representation of local geometric features,which makes our network perform *** model performs well on several basic point clouds processing tasks such as classification,segmentation,and normal estimation,demonstrating the effectiveness and superiority of our ***,we provide ablation experiments and visualizations to better understand our network.

关键词： deep learning point-based models point cloud analysis 3d shape analysis point cloud processing

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Missing and murdered indigenous women and girls in Canada:a new population affinity assessment technique to aid in identification using 3d technology

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Forensic Sciences Research 2022年第3期7卷 427-439页

作者： Elisabeth Cuerrier-Richer department of Anthropology University of TorontoTorontoOntarioCanada

As of 2015,204 cases of missing and murdered Indigenous women and girls(MMIWG)remained unsolved in Canada,making it a major concern for Canadian Indigenous communities,who are still pressing for the resolution of these *** forensic anthropology,the assessment of population affinity can be useful to help identify *** affinity,previously referred to as ancestry,is evaluated based on morphological analyses,which examine the size and shape of skeletal features,and metric analyses,which utilise skeletal ***,morphological analyses strongly depend on an anthropologist’s experience with human variation,which makes the analyses particularly challenging to reproduce and *** purpose of this study is to improve the rigour of morphological analyses by using 3d technology to quantify relevant cranial nonmetric population affinity *** there is currently little morphological data available for the Canadian Indigenous population,this research aims to develop a new technique that could aid in the identification of *** study comprised a total of 87 adult female crania,including 24 of Canadian Inuit origin,50 of European descent and 13 of African *** samples were imaged using photogrammetry,then analysed using a 3d shape analysis in 3dS *** show that this method is satisfactory in correctly evaluating population affinity with an accuracy of 87.36%(jackknifed:80.46%)and an average repeatability of 97%.Unfortunately,the small Canadian Indigenous sample size impacted the applicability of the results and further research will be required before the technique can be used to aid in the identification of MMIWG in Canada.

关键词： Forensic sciences forensic anthropology population affinity assessment Canadian Indigenous morphological analyses cranial traits 3d shape analysis

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