计算机辅助设计与图形学学报最新文献_第10页

A Semantic Segmentation Method of In-Vehicle Small Targets Point CloudBased on Improved RangeNet++ Loss Function 基于改进rangenet++损失函数的车载小目标点云语义分割方法

计算机辅助设计与图形学学报 Pub Date : 2021-05-01 DOI: 10.3724/sp.j.1089.2021.18581

Shuo Zhang, Q. Ye, Jing Shi, Hang Liu

引用次数: 1

Digital Twin Registration Technique of Spatial Augmented Reality for Tangible Interaction 用于有形交互的空间增强现实数字孪生配准技术

计算机辅助设计与图形学学报 Pub Date : 2021-05-01 DOI: 10.3724/sp.j.1089.2021.18556

Zhigeng Pan, Jiali Gao, Ruonan Wang, Qingshu Yuan, Ran Fan, Ling She

{"title":"Digital Twin Registration Technique of Spatial Augmented Reality for Tangible Interaction","authors":"Zhigeng Pan, Jiali Gao, Ruonan Wang, Qingshu Yuan, Ran Fan, Ling She","doi":"10.3724/sp.j.1089.2021.18556","DOIUrl":"https://doi.org/10.3724/sp.j.1089.2021.18556","url":null,"abstract":": In spatial augmented reality (SAR) systems supporting tangible interaction, user interactions can cause rapid changes in orientation and position of interaction objects. To ensure the efficiency and accuracy of registration during objects movements, a digital twin registration technique for SAR is proposed. Models are made in the digital space and their geometric parameters are entirely consistent with the objects in the physical space. The orientation and position parameters of the physical objects are tracked in real-time during user interaction. Then the digital objects are adjusted according the parameters. Moreover, intrinsic and extrinsic parameters of the pro-jector, which are calibrated in advance, are used to set the virtual camera in the digital space. The projection pat-terns are rendered in that virtual camera and then projected onto the interaction objects. The efficiency and accuracy are evaluated in the middle school physical experiment learning magnetic induction line based on the projection interaction tabletop, which meet the requirements of tangible interaction in SAR systems.","PeriodicalId":52442,"journal":{"name":"计算机辅助设计与图形学学报","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42999616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Bounding Box Regression Based Image Composition Recommendation 基于边界盒回归的图像合成推荐

计算机辅助设计与图形学学报 Pub Date : 2021-05-01 DOI: 10.3724/sp.j.1089.2021.18560

Guoye Yang, Wen-Yang Zhou, Lan Liu, Songhai Zhang

引用次数: 0

Image Inpainting Using Channel Attention and Hierarchical Residual Networks 基于通道注意力和层次残差网络的图像修复

计算机辅助设计与图形学学报 Pub Date : 2021-05-01 DOI: 10.3724/sp.j.1089.2021.18514

Hao Yang, Yingzhen Yu

引用次数: 4

Unsupervised 3D Object Retrieval in Loop View 环视图中的无监督3D对象检索

计算机辅助设计与图形学学报 Pub Date : 2021-05-01 DOI: 10.3724/sp.j.1089.2021.18636

Zhenzhong Kuang, Jie Yang, Jun Yu

引用次数: 0

Visual Analysis System for Search Trend Data 搜索趋势数据的可视化分析系统

计算机辅助设计与图形学学报 Pub Date : 2021-04-01 DOI: 10.3724/sp.j.1089.2021.18810

Danny Cheng, Yunhai Wang

引用次数: 1

Power Oriented Optimization for the Defect-Tolerant Mapping of CMOL Circuits 面向功率的CMOL电路容错映射优化

计算机辅助设计与图形学学报 Pub Date : 2021-04-01 DOI: 10.3724/sp.j.1089.2021.18525

Shangluan Xie, Yinshui Xia, Xiaojing Zha

引用次数: 0

Construction of Feature Tensor Descriptor and Self-Similarity Analysis for 3D Point Cloud Models 三维点云模型特征张量描述子的构建与自相似度分析

计算机辅助设计与图形学学报 Pub Date : 2021-04-01 DOI: 10.3724/sp.j.1089.2021.18542

Hailong Hu, Zhong Li, S. Qin, Li-zhuang Ma

{"title":"Construction of Feature Tensor Descriptor and Self-Similarity Analysis for 3D Point Cloud Models","authors":"Hailong Hu, Zhong Li, S. Qin, Li-zhuang Ma","doi":"10.3724/sp.j.1089.2021.18542","DOIUrl":"https://doi.org/10.3724/sp.j.1089.2021.18542","url":null,"abstract":"Local self-similarity of 3D model is a fundamental problem in the shape analysis. The construction of a local shape descriptor is very important to the final result of self-similarity analysis. To solve this problem, a self-similarity analysis method based on the tensor fusion feature descriptor is proposed. Firstly, the shape diameter function (SDF) of a point cloud model is approximately calculated by using relevant facets and antipodal points. Then, spectral clustering is used to segment the model into sub-blocks, and the three-dimensional feature tensor is constructed from the SDF, shape index (SI) and Gauss curvature (GS) matrix of KNN neighborhood points. Finally, the shape descriptor is obtained by constructing the mapping with the tensor norm, and then the similarity measure is defined and the self-similarity between the sub-blocks of the model is analyzed. Several state-of-the-art methods (including partial matching and saliency detection) are 第 4 期胡海龙, 等: 三维点云模型特征张量描述符的构造及自相似性分析 591 tested. In terms of not only the visual effect, but also the similarity measure and the relative errors, the results show that this method can effectively describe the shape and improves the recognition accuracy of similar sub-blocks of a point cloud model.","PeriodicalId":52442,"journal":{"name":"计算机辅助设计与图形学学报","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48482414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A SLAM Pose Graph Optimization Method Using Dual Visual Odometry 基于双视觉里程计的SLAM姿态图优化方法

计算机辅助设计与图形学学报 Pub Date : 2021-04-01 DOI: 10.3724/SP.J.1089.2021.18663

Jianfei Cao, Jincheng Yu, S. Pan, Feng Gao, Chao Yu, Zhilin Xu, Zhengfeng Huang, Yu Wang

{"title":"A SLAM Pose Graph Optimization Method Using Dual Visual Odometry","authors":"Jianfei Cao, Jincheng Yu, S. Pan, Feng Gao, Chao Yu, Zhilin Xu, Zhengfeng Huang, Yu Wang","doi":"10.3724/SP.J.1089.2021.18663","DOIUrl":"https://doi.org/10.3724/SP.J.1089.2021.18663","url":null,"abstract":", Abstract: Backend trajectory optimization is an important part of the visual simultaneous localization and mapping system, which can significantly improve localization accuracy. However, the existing optimization methods based on the bundle adjustment have a large amount of calculation in large scenes and cannot be applied to end-to-end visual odometries. To solve this problem, a universal backend pose graph optimization algorithm with two visual odometries at the front end is proposed, which can be applied to end-to-end visual odometries. This method uses a high-speed but low-precision end-to-end visual odometry to run at high frequency, while a low-speed but high-precision visual odometry runs at a low frequency. Local optimization uses Gauss-Newton method iterative optimization through the constraints provided by two odometries. Global optimization is per-formed simultaneously which based on key frames scene matching. Experiments show that the simultaneous localization and mapping system which apply this optimization method can run in real-time on the KITTI dataset. Compared with the two visual odometries, the accuracy has been greatly improved. And compared with several well-known open source simultaneous localization and mapping methods that apply backend trajectory optimization, low errors have been achieved in trajectory error, absolute translational error, rotation error and rela-tive pose error, taking into account the advantages of the accuracy of traditional methods and the advantages of high speed end-to-end methods. In addition, the optimization framework can also be applied to other more visual odometries.","PeriodicalId":52442,"journal":{"name":"计算机辅助设计与图形学学报","volume":"33 1","pages":"1264-1272"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42660500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Survey of Word Cloud Visualization 词云可视化研究综述

计算机辅助设计与图形学学报 Pub Date : 2021-04-01 DOI: 10.3724/sp.j.1089.2021.18811

Chen Bao, Yunhai Wang

引用次数: 4