咨询与建议

限定检索结果

文献类型

  • 50 篇 期刊文献
  • 46 篇 会议
  • 2 篇 学位论文

馆藏范围

  • 98 篇 电子文献
  • 0 种 纸本馆藏

日期分布

学科分类号

  • 94 篇 工学
    • 67 篇 计算机科学与技术...
    • 33 篇 电气工程
    • 11 篇 测绘科学与技术
    • 9 篇 控制科学与工程
    • 9 篇 软件工程
    • 7 篇 信息与通信工程
    • 7 篇 土木工程
    • 6 篇 建筑学
    • 5 篇 环境科学与工程(可...
    • 4 篇 交通运输工程
    • 3 篇 生物医学工程(可授...
    • 2 篇 机械工程
    • 2 篇 仪器科学与技术
    • 1 篇 动力工程及工程热...
    • 1 篇 电子科学与技术(可...
    • 1 篇 石油与天然气工程
    • 1 篇 安全科学与工程
    • 1 篇 网络空间安全
  • 23 篇 医学
    • 20 篇 临床医学
    • 5 篇 特种医学
    • 1 篇 基础医学(可授医学...
  • 13 篇 理学
    • 7 篇 地球物理学
    • 3 篇 生物学
    • 2 篇 数学
    • 1 篇 物理学
    • 1 篇 化学
    • 1 篇 地理学
  • 3 篇 农学
    • 3 篇 作物学
  • 3 篇 管理学
    • 3 篇 管理科学与工程(可...
  • 2 篇 文学
    • 2 篇 新闻传播学

主题

  • 98 篇 3d semantic segm...
  • 16 篇 point cloud
  • 15 篇 deep learning
  • 10 篇 three-dimensiona...
  • 10 篇 point clouds
  • 9 篇 3d object detect...
  • 7 篇 semantics
  • 6 篇 semantic segment...
  • 6 篇 3d instance segm...
  • 5 篇 task analysis
  • 5 篇 lidar
  • 5 篇 multi-modal fusi...
  • 4 篇 autonomous drivi...
  • 4 篇 3d point cloud
  • 4 篇 3d scene underst...
  • 4 篇 feature extracti...
  • 4 篇 point cloud comp...
  • 3 篇 unsupervised dom...
  • 3 篇 transformers
  • 3 篇 3d reconstructio...

机构

  • 3 篇 univ chinese aca...
  • 3 篇 univ hong kong p...
  • 3 篇 tsinghua univ pe...
  • 3 篇 east china norma...
  • 2 篇 swiss fed inst t...
  • 2 篇 yonsei univ sch ...
  • 2 篇 chinese acad sci...
  • 2 篇 southeast univ s...
  • 2 篇 hku peoples r ch...
  • 2 篇 china univ geosc...
  • 2 篇 xiongan inst inn...
  • 2 篇 china univ geosc...
  • 2 篇 tech univ munich...
  • 2 篇 univ chinese aca...
  • 2 篇 natl univ singap...
  • 1 篇 east china norma...
  • 1 篇 karlsruhe inst t...
  • 1 篇 casia sensetime ...
  • 1 篇 sharper shape sa...
  • 1 篇 cuhk sz peoples ...

作者

  • 4 篇 jiang li
  • 4 篇 wu xiaoyang
  • 3 篇 kim hyoungkwan
  • 3 篇 qu yanyun
  • 3 篇 xie yuan
  • 3 篇 kim juhyeon
  • 3 篇 zhang xiaolin
  • 3 篇 kim yohan
  • 3 篇 li jiamao
  • 3 篇 zhao hengshuang
  • 2 篇 ni peizhou
  • 2 篇 peters torben
  • 2 篇 zhang guanghui
  • 2 篇 litany or
  • 2 篇 jiang tao
  • 2 篇 peng bohao
  • 2 篇 lee gim hee
  • 2 篇 weigel hendrik
  • 2 篇 wan jie
  • 2 篇 beyerer juergen

语言

  • 94 篇 英文
  • 4 篇 其他
检索条件"主题词=3D semantic segmentation"
98 条 记 录,以下是91-100 订阅
排序:
3d Shape segmentation with Geometric deep Learning  20th
3D Shape Segmentation with Geometric Deep Learning
收藏 引用
20th International Conference on Image Analysis and Processing (ICIAP)
作者: Boscaini, davide Poiesi, Fabio Fdn Bruno Kessler Technol Vis Via Sommarive 18 I-38123 Trento Italy
The semantic segmentation of 3d shapes with a high-density of vertices could be impractical due to large memory requirements. To make this problem computationally tractable, we propose a neural-network based approach ... 详细信息
来源: 评论
RANP: Resource Aware Neuron Pruning at Initialization for 3d CNNs  8
RANP: Resource Aware Neuron Pruning at Initialization for 3D...
收藏 引用
8th International Conference on 3d Vision (3dV)
作者: Xu, Zhiwei Ajanthan, Thalaiyasingam Vineet, Vibhav Hartley, Richard Australian Natl Univ Canberra ACT Australia Australian Ctr Robot Vis Canberra ACT Australia Microsoft Res Redmond WA USA CSIRO Data61 Brisbane Qld Australia
Although 3d Convolutional Neural Networks (CNNs) are essential for most learning based applications involving dense 3d data, their applicability is limited due to excessive memory and computational requirements. Compr... 详细信息
来源: 评论
Breaking Speed Limits with Simultaneous Ultra-Fast MRI Reconstruction and Tissue segmentation  3
Breaking Speed Limits with Simultaneous Ultra-Fast MRI Recon...
收藏 引用
3rd Conference on Medical Imaging with deep Learning (MIdL)
作者: Caliva, Francesco Leynes, Andrew P. Shah, Rutwik Bharadwaj, Upasana Upadhyay Majumdar, Sharmila Larson, Peder E. Z. Pedoia, Valentina Univ Calif San Francisco Dept Radiol & Biomed Imaging Ctr Intelligent Imaging San Francisco CA 94143 USA UC Berkeley UC San Francisco Joint Grad Program B San Francisco CA USA
Magnetic Resonance Image (MRI) acquisition, reconstruction and tissue segmentation are usually considered separate problems. This can be limiting when it comes to rapidly extracting relevant clinical parameters. In ma... 详细信息
来源: 评论
2d-3d scene understanding for autonomous driving: Compréhension 2d-3d de scènes pour la conduite autonome
2D-3D scene understanding for autonomous driving: Compréhen...
收藏 引用
作者: Jaritz, Maximilian Paris Sciences et Lettres
学位级别:博士
dans cette th32;se, nous abordons les d33;fis de la raret33; des annotations et la fusion de donn33;es h33;t33;rog32;nes tels que les nuages de points 3d et images 2d. d’abord, nous adoptons une ... 详细信息
来源: 评论
Automatic segmentation of the Prostate on 3d CT Images by Using Multiple deep Learning Networks  18
Automatic Segmentation of the Prostate on 3D CT Images by Us...
收藏 引用
5th International Conference on Biomedical and Bioinformatics Engineering (ICBBE)
作者: Xiong, Jiayang Jiang, Luan Li, Qiang Shanghai Jiao Tong Univ 800 Dongchuan Shanghai Peoples R China Shanghai United Imaging Healthcare Co Ltd 2258 Chengbei Rd Shanghai 201815 Peoples R China
Automatic segmentation of the prostate on CT images has many applications in prostate cancer diagnosis and therapy. However, prostate segmentation from CT images is a very challenging task due to the low contrast of s... 详细信息
来源: 评论
GLSNet: Global and Local Streams Network for 3d Point Cloud Classification
GLSNet: Global and Local Streams Network for 3D Point Cloud ...
收藏 引用
IEEE Applied Imagery Pattern Recognition Workshop (AIPR)
作者: Bao, Rina Palaniappan, Kannappan Zhao, Yunxin Seetharaman, Guna Zeng, Wenjun Univ Missouri Columbia MO 65211 USA Naval Res Lab Washington DC 20375 USA
We propose a novel deep architecture for semantic labeling of 3d point clouds referred to as Global and Local Streams Network (GLSNet) which is designed to capture both global and local structures and contextual infor... 详细信息
来源: 评论
GLSNet: Global and Local Streams Network for 3d Point Cloud Classification
GLSNet: Global and Local Streams Network for 3D Point Cloud ...
收藏 引用
Applied Imagery Pattern Recognition Workshop (AIPR)
作者: Rina Bao Kannappan Palaniappan Yunxin Zhao Guna Seetharaman Wenjun Zeng Dept. of Electrical Engineering and Computer Science University of Missouri Columbia MO USA Naval Research Laboratory Washington D.C. USA
We propose a novel deep architecture for semantic labeling of 3d point clouds referred to as Global and Local Streams Network (GLSNet) which is designed to capture both global and local structures and contextual infor... 详细信息
来源: 评论
A Novel Approach Based on Cluster-group for Classification of 3d Residential Scene
A Novel Approach Based on Cluster-group for Classification o...
收藏 引用
International Conference on Information Science, Electronics and Electrical Engineering (ISEEE)
作者: Lu, Guiliang Zhou, Yu Yu, Yao du, Sidan Nanjing Univ Sch Elect Sci & Engn Nanjing Peoples R China
To understand scenes and help autonomous robots and cars, researchers' attention is directed through the problem of classifying 3d point cloud. In this paper, we present a novel approach to semantically segment 3d... 详细信息
来源: 评论