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11-20 订阅
排序:
Language-Grounded Indoor 3d semantic segmentation in the Wild  1
收藏 引用
17th European Conference on Computer Vision (ECCV)
作者: Rozenberszki, david Litany, Or dai, Angela Tech Univ Munich Munich Germany NVIDIA Santa Clara CA USA
Recent advances in 3d semantic segmentation with deep neural networks have shown remarkable success, with rapid performance increase on available datasets. However, current 3d semantic segmentation benchmarks contain ... 详细信息
来源: 评论
dOdA: data-Oriented Sim-to-Real domain Adaptation for 3d semantic segmentation  1
收藏 引用
17th European Conference on Computer Vision (ECCV)
作者: ding, Runyu Yang, Jihan Jiang, Li Qi, Xiaojuan Univ Hong Kong Hong Kong Peoples R China MPI Informat Saarbrucken Germany
deep learning approaches achieve prominent success in 3d semantic segmentation. However, collecting densely annotated real-world 3d datasets is extremely time-consuming and expensive. Training models on synthetic data... 详细信息
来源: 评论
Cross-domain and Cross-Modal Knowledge distillation in domain Adaptation for 3d semantic segmentation  22
Cross-Domain and Cross-Modal Knowledge Distillation in Domai...
收藏 引用
30th ACM International Conference on Multimedia (MM)
作者: Li, Miaoyu Zhang, Yachao Xie, Yuan Gao, Zuodong Li, Cuihua Zhang, Zhizhong Qu, Yanyun Xiamen Univ Xiamen Peoples R China East China Normal Univ Shanghai Peoples R China
With the emergence of multi-modal datasets where LidAR and camera are synchronized and calibrated, cross-modal Unsupervised domain Adaptation (UdA) has attracted increasing attention because it reduces the laborious a... 详细信息
来源: 评论
Learning 3d semantic segmentation with only 2d Image Supervision  9
Learning 3D Semantic Segmentation with only 2D Image Supervi...
收藏 引用
9th International Conference on 3d Vision (3dV)
作者: Genova, Kyle Yin, Xiaoqi Kundu, Abhijit Pantofaru, Caroline Cole, Forrester Sud, Avneesh Brewington, Brian Shucker, Brian Funkhouser, Thomas Google Res Mountain View CA 94043 USA Princeton Univ Princeton NJ 08544 USA
With the recent growth of urban mapping and autonomous driving efforts, there has been an explosion of raw 3d data collected from terrestrial platforms with lidar scanners and color cameras. However, due to high label... 详细信息
来源: 评论
Cross-modal Unsupervised domain Adaptation for 3d semantic segmentation via Bidirectional Fusion-then-distillation  23
Cross-modal Unsupervised Domain Adaptation for 3D Semantic S...
收藏 引用
31st ACM International Conference on Multimedia (MM)
作者: Wu, Yao Xing, Mingwei Zhang, Yachao Xie, Yuan Fan, Jianping Shi, Zhongchao Qu, Yanyun Xiamen Univ Sch Informat Xiamen Peoples R China Xiamen Univ Inst Artificial Intelligence Xiamen Peoples R China Tsinghua Univ Shenzhen Peoples R China East China Normal Univ Shanghai Peoples R China East China Normal Univ Chongqing Inst Chongqing Peoples R China Lenovo Res Beijing Peoples R China
Cross-modal Unsupervised domain Adaptation (UdA) becomes a research hotspot because it reduces the laborious annotation of target domain samples. Existing methods only mutually mimic the outputs of cross-modality in e... 详细信息
来源: 评论
Open-Vocabulary 3d semantic segmentation with Foundation Models
Open-Vocabulary 3D Semantic Segmentation with Foundation Mod...
收藏 引用
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
作者: Jiang, Li Shi, Shaoshuai Schiele, Bernt Max Planck Inst Informat Saarland Informat Campus Saarbrucken Germany
In dynamic 3d environments, the ability to recognize a diverse range of objects without the constraints of predefined categories is indispensable for real-world applications. In response to this need, we introduce OV3... 详细信息
来源: 评论
Iterative deep Fusion for 3d semantic segmentation  4
Iterative Deep Fusion for 3D Semantic Segmentation
收藏 引用
4th IEEE International Conference on Robotic Computing (IEEE IRC)
作者: duerr, Fabian Weigel, Hendrik Maehlisch, Mirko Beyerer, Juergen AUDI AG Dept Sensor Fus & MapLearning Automated Driving Ingolstadt Germany Fraunhofer Ctr Machine Learning Fraunhofer Inst Optron Syst Technol & Image Exploitat IOSB Karlsruhe Germany Karlsruhe Inst Technol Vis & Fus Lab KIT Karlsruhe Germany
Understanding and interpreting a scene is a key task of environment perception for autonomous driving, which is why autonomous vehicles are equipped with a wide range of different sensors. semantic segmentation of sen... 详细信息
来源: 评论
CLFusion:3d semantic segmentation Based on Camera and Lidar Fusion
CLFusion:3D Semantic Segmentation Based on Camera and Lidar ...
收藏 引用
IEEE International Symposium on Circuits and Systems (ISCAS)
作者: Wang, Tianyue Song, Rujun Xiao, Zhuoling Yan, Bo Qin, Haojie He, di Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Peoples R China Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Peoples R China
In the field of autonomous driving, semantic segmentation is crucial for scene understanding. Currently, there are two main methods: camera-based and Lidar-based approaches. To address the issues of Lidar segmentation... 详细信息
来源: 评论
ROBUST 3d semantic segmentation WITH INCOMPLETE POINT CLOUdS BASEd ON SEQUENTIAL FRAME SAMPLING  31
ROBUST 3D SEMANTIC SEGMENTATION WITH INCOMPLETE POINT CLOUDS...
收藏 引用
2024 International Conference on Image Processing
作者: Yamaguchi, Masahiro Higa, Kyota Hosoi, Toshinori Shibata, Takashi NEC Corp Ltd Visual Intelligence Res Labs Shimonumabe 1753Nakahara Ku Kawasaki Kanagawa Japan
This paper proposes a method for learning 3d semantic segmentation robust to incomplete point clouds. Our method first generates pseud-incomplete point clouds from original 3d point clouds by sequential frame sampling... 详细信息
来源: 评论
Adaptive Margin Contrastive Learning for Ambiguity-aware 3d semantic segmentation
Adaptive Margin Contrastive Learning for Ambiguity-aware 3D ...
收藏 引用
IEEE International Conference on Multimedia and Expo (ICME)
作者: Chen, Yang duane, Yueqi Zhang, Runzhong Tan, Yap-Peng Nanyang Technol Univ Singapore Singapore Tsinghua Univ Beijing Peoples R China
In this paper, we propose an adaptive margin contrastive learning method for 3d point cloud semantic segmentation, namely AMContrast3d. Most existing methods use equally penalized objectives, which ignore per-point am... 详细信息
来源: 评论