咨询与建议

限定检索结果

文献类型

  • 425 篇 期刊文献
  • 11 篇 会议

馆藏范围

  • 436 篇 电子文献
  • 0 种 纸本馆藏

日期分布

学科分类号

  • 436 篇 工学
    • 423 篇 电气工程
    • 421 篇 控制科学与工程
    • 12 篇 计算机科学与技术...
    • 6 篇 仪器科学与技术
    • 3 篇 信息与通信工程
    • 1 篇 土木工程
    • 1 篇 交通运输工程
    • 1 篇 生物医学工程(可授...
  • 3 篇 理学
    • 2 篇 化学
    • 2 篇 生物学
    • 1 篇 物理学
  • 2 篇 医学
    • 2 篇 临床医学
  • 1 篇 管理学
    • 1 篇 管理科学与工程(可...

主题

  • 436 篇 deep learning fo...
  • 77 篇 feature extracti...
  • 67 篇 three-dimensiona...
  • 58 篇 object detection
  • 55 篇 training
  • 51 篇 visual learning
  • 46 篇 task analysis
  • 42 篇 localization
  • 42 篇 computer vision ...
  • 41 篇 cameras
  • 40 篇 semantic scene u...
  • 40 篇 deep learning me...
  • 40 篇 rgb-d perception
  • 39 篇 robots
  • 38 篇 segmentation and...
  • 38 篇 semantics
  • 38 篇 computer vision ...
  • 36 篇 visualization
  • 31 篇 point cloud comp...
  • 30 篇 recognition

机构

  • 6 篇 google ch-8002 z...
  • 5 篇 univ bonn d-5311...
  • 5 篇 korea adv inst s...
  • 5 篇 zhejiang univ co...
  • 5 篇 tech univ munich...
  • 4 篇 univ tubingen d-...
  • 4 篇 keio univ yokoha...
  • 4 篇 carnegie mellon ...
  • 4 篇 univ chinese aca...
  • 4 篇 nyu brooklyn ny ...
  • 3 篇 univ michigan an...
  • 3 篇 shanghai jiao to...
  • 3 篇 zhejiang univ zh...
  • 3 篇 natl univ def te...
  • 3 篇 univ chinese aca...
  • 3 篇 shanghai jiao to...
  • 3 篇 southeast univ s...
  • 3 篇 toyota res inst ...
  • 3 篇 univ perugia dep...
  • 3 篇 alibaba grp peop...

作者

  • 8 篇 tombari federico
  • 8 篇 giusti alessandr...
  • 8 篇 stachniss cyrill
  • 7 篇 behley jens
  • 7 篇 sugiura komei
  • 6 篇 caputo barbara
  • 5 篇 guzzi jerome
  • 5 篇 seo seung-woo
  • 5 篇 weyler jan
  • 5 篇 navab nassir
  • 5 篇 hutter marco
  • 5 篇 wu jun
  • 5 篇 xiang zhiyu
  • 5 篇 valada abhinav
  • 4 篇 shin ukcheol
  • 4 篇 van gool luc
  • 4 篇 gambardella luca...
  • 4 篇 nava mirko
  • 4 篇 garg sourav
  • 4 篇 wang yue

语言

  • 436 篇 英文
检索条件"主题词=Deep Learning for Visual Perception"
436 条 记 录,以下是81-90 订阅
排序:
Edge Enhanced Implicit Orientation learning With Geometric Prior for 6D Pose Estimation
收藏 引用
IEEE ROBOTICS AND AUTOMATION LETTERS 2020年 第3期5卷 4931-4938页
作者: Wen, Yilin Pan, Hao Yang, Lei Wang, Wenping Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Microsoft Res Asia Beijing 100080 Peoples R China
Estimating 6D poses of rigid objects from RGB images is an important but challenging task. This is especially true for textureless objects with strong symmetry, since they have only sparse visual features to be levera... 详细信息
来源: 评论
MOLTR: Multiple Object Localization, Tracking and Reconstruction From Monocular RGB Videos
收藏 引用
IEEE ROBOTICS AND AUTOMATION LETTERS 2021年 第2期6卷 3341-3348页
作者: Li, Kejie Rezatofighi, Hamid Reid, Ian Univ Adelaide Sch Comp Sci Adelaide SA 5005 Australia Australian Ctr Robot Vis Adelaide SA 5005 Australia Monash Univ Fac Informat Technol Dept Data Sci & AI Melbourne Vic 3800 Australia
Semantic aware reconstruction is more advantageous than geometric-only reconstruction for future robotic and AR/VR applications because it represents not only where things are, but also what things are. Object-centric... 详细信息
来源: 评论
Simultaneously learning Corrections and Error Models for Geometry-Based visual Odometry Methods
收藏 引用
IEEE ROBOTICS AND AUTOMATION LETTERS 2020年 第4期5卷 6536-6543页
作者: De Maio, Andrea Lacroix, Simon Univ Toulouse LAAS CNRS F-31031 Toulouse France
This letter fosters the idea that deep learning methods can be used to complement classical visual odometry pipelines to improve their accuracy and to associate uncertainty models to their estimations. We show that th... 详细信息
来源: 评论
Collaborative Multi-Object Tracking With Conformal Uncertainty Propagation
收藏 引用
IEEE ROBOTICS AND AUTOMATION LETTERS 2024年 第4期9卷 3323-3330页
作者: Su, Sanbao Han, Songyang Li, Yiming Zhang, Zhili Feng, Chen Ding, Caiwen Miao, Fei Univ Connecticut Dept Comp Sci & Engn Storrs CT 06268 USA Univ Connecticut Storrs CT 06268 USA NYU Tandon Sch Engn Brooklyn NY 11201 USA
Object detection and multiple object tracking (MOT) are essential components of self-driving systems. Accurate detection and uncertainty quantification are both critical for onboard modules, such as perception, predic... 详细信息
来源: 评论
learning Rearrangement Manipulation via Scene Prediction in Point Cloud
收藏 引用
IEEE ROBOTICS AND AUTOMATION LETTERS 2024年 第12期9卷 11090-11097页
作者: Ma, Anji Duan, Xingguang Beijing Inst Technol Sch Mechatron Engn Beijing 100081 Peoples R China
Predicting scene evolution conditioned on robotic actions is a vital technique in modeling robot manipulations. Previous studies have primarily focused on learning spatiotemporally continuous actions like Cartesian di... 详细信息
来源: 评论
learning-To-Rank Approach for Identifying Everyday Objects Using a Physical-World Search Engine
收藏 引用
IEEE ROBOTICS AND AUTOMATION LETTERS 2024年 第3期9卷 2088-2095页
作者: Kaneda, Kanta Nagashima, Shunya Korekata, Ryosuke Kambara, Motonari Sugiura, Komei Keio Univ Yokohama 2238522 Japan
Domestic service robots offer a solution to the increasing demand for daily care and support. A human-in-the-loop approach that combines automation and operator intervention is considered to be a realistic approach to... 详细信息
来源: 评论
DiffMap: Enhancing Map Segmentation With Map Prior Using Diffusion Model
收藏 引用
IEEE ROBOTICS AND AUTOMATION LETTERS 2024年 第11期9卷 9836-9843页
作者: Jia, Peijin Wen, Tuopu Luo, Ziang Yang, Mengmeng Jiang, Kun Liu, Ziyuan Tang, Xuewei Lei, Zhiquan Cui, Le Zhang, Bo Sheng, Kehua Yang, Diange Tsinghua Univ Sch Vehicle & Mobil Beijing 100084 Peoples R China DiDi Chuxing Tianjin 300450 Peoples R China
Constructing high-definition (HD) maps is a crucial requirement for enabling autonomous driving. In recent years, several map segmentation algorithms have been developed to address this need, leveraging advancements i... 详细信息
来源: 评论
Delta Descriptors: Change-Based Place Representation for Robust visual Localization
收藏 引用
IEEE ROBOTICS AND AUTOMATION LETTERS 2020年 第4期5卷 5120-5127页
作者: Garg, Sourav Harwood, Ben Anand, Gaurangi Milford, Michael Queensland Univ Technol Sch Elect Engn & Robot Brisbane Qld Australia Australian Ctr Robot Vis Brisbane Qld 4001 Australia QUT Ctr Robot Brisbane Qld 4001 Australia Monash Univ Dept Elect & Comp Syst Engn Clayton Vic 3800 Australia Queensland Univ Technol Sch Comp Sci Brisbane Qld 4001 Australia
visual place recognition is challenging because there are so many factors that can cause the appearance of a place to change, from day-night cycles to seasonal change to atmospheric conditions. In recent years a large... 详细信息
来源: 评论
DenseLiDAR: A Real-Time Pseudo Dense Depth Guided Depth Completion Network
收藏 引用
IEEE ROBOTICS AND AUTOMATION LETTERS 2021年 第2期6卷 1808-1815页
作者: Gu, Jiaqi Xiang, Zhiyu Ye, Yuwen Wang, Lingxuan Zhejiang Univ Coll Informat Sci & Eletron Engn Hangzhou 310027 Peoples R China Zhejiang Univ Zhejiang Prov Key Lab Informat Proc Commun & Netw Hangzhou 310027 Peoples R China
Depth Completion can produce a dense depth map from a sparse input and provide a more complete 3D description of the environment. Despite great progress made in depth completion, the sparsity of the input and low dens... 详细信息
来源: 评论
SE3ET: SE(3)-Equivariant Transformer for Low-Overlap Point Cloud Registration
收藏 引用
IEEE ROBOTICS AND AUTOMATION LETTERS 2024年 第11期9卷 9526-9533页
作者: Lin, Chien Erh Zhu, Minghan Ghaffari, Maani Univ Michigan Ann Arbor MI 48109 USA
Partial point cloud registration is a challenging problem in robotics, especially when the robot undergoes a large transformation, causing a significant initial pose error and a low overlap between measurements. This ... 详细信息
来源: 评论