Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

RAD51 gene is associated with advanced age-related macular degeneration in Chinese population.

Clinical biochemistry·2013
Same author

Immunization against recombinant GnRH-I alters ultrastructure of gonadotropin cell in an experimental boar model.

Reproductive biology and endocrinology : RB&E·2013
Same author

Multi-class constrained normalized cut with hard, soft, unary and pairwise priors and its applications to object segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2013
Same author

Comparison of genomic and amino acid sequences of eight Japanese encephalitis virus isolates from bats.

Archives of virology·2013
Same author

Regulation of dendritic cell differentiation in bone marrow during emergency myelopoiesis.

Journal of immunology (Baltimore, Md. : 1950)·2013
Same author

Separation of mandelic acid and its derivatives with new immobilized cellulose chiral stationary phase.

Journal of Zhejiang University. Science. B·2013
Same journal

Relation DETR+: Exploring Explicit Position Relation Prior for Dense Prediction.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

RBF++: Quantifying and Optimizing Reasoning Boundaries across Measurable and Unmeasurable Capabilities for Chain-of-Thought Reasoning.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

CAFE: Cross-View Adaptive Fusion and Cluster Center Enhancement for Robust Multi-View Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Learning Shape Anchors for Holistic Indoor Scene Understanding.

IEEE transactions on pattern analysis and machine intelligence·2026
查看所有相关文章

相关实验视频

Updated: Jul 9, 2025

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.4K

学习动态场景条件的3D物体探测器

Yu Zheng, Yueqi Duan, Zongtai Li

    IEEE transactions on pattern analysis and machine intelligence
    |November 28, 2023
    PubMed
    概括
    此摘要是机器生成的。

    HyperDet3D和HyperFormer3D引入了场景级知识,用于动态的3D对象检测. 这些方法通过适应场景背景来提高准确性,克服对象级分析的局限性.

    更多相关视频

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    559
    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
    08:25

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

    Published on: May 7, 2019

    9.0K

    相关实验视频

    Last Updated: Jul 9, 2025

    A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
    05:41

    A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

    Published on: February 6, 2020

    9.4K
    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    559
    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
    08:25

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

    Published on: May 7, 2019

    9.0K

    科学领域:

    • 计算机视觉 计算机视觉
    • 人工智能的人工智能
    • 机器人技术 机器人技术 机器人技术

    背景情况:

    • 现有的3D物体探测器在复杂场景中因缺乏场景级上下文而扎着模糊性.
    • 单独对象级分析不足以准确区分类似的对象,仅基于点数据.

    研究的目的:

    • 开发一个动态的3D物体探测器 (HyperDet3D),利用场景级别的知识提高精度.
    • 通过结合特定场景的先验来增强探测器,以适应不同的环境.
    • 提出HyperFormer3D,解决场景输入中的噪音和冗余,以实现更强大的参数生成.

    主要方法:

    • 设计了场景条件化的超级网络,以学习场景无意识的嵌入和场景特定的知识.
    • 介绍了特定任务的场景先验:语义事件和对象定位.
    • 在HyperFormer3D中开发了一个基于变压器的超级网络,将场景先验转化为探测器参数,减轻噪声.

    主要成果:

    • 通过使用层次化的场景背景,HyperDet3D有效地减少了3D对象表示中的模两可.
    • 通过利用聚焦的场景先验,避免原始场景数据问题,HyperFormer3D展示了改进的性能.
    • 这两种方法在ScanNet,SUN RGB-D和MatterPort3D数据集上都显示出显著的有效性.

    结论:

    • 场景级的先验对于提高3D物体探测器的性能和稳定性至关重要.
    • 通过结合特定任务的场景理解,HyperFormer3D提供了更精细的方法,从而带来了卓越的检测能力.
    • 提出的方法代表了复杂环境中的动态3D物体检测的重大进步.