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

Three-Dimensional Analysis of Strain01:29

Three-Dimensional Analysis of Strain

Three-dimensional strain analysis is crucial for understanding how materials deform under stress, particularly in elastic, homogeneous materials. This method employs principal stress axes to simplify complex stress states into more understandable forms. Subjected to stress, a small cubic element within a material either expands or contracts along these axes, transforming into a rectangular parallelepiped. This transformation effectively illustrates the material's deformation. The principal...
Sight Distance in a Vertical Curve01:29

Sight Distance in a Vertical Curve

Sight distance on vertical curves is critical in roadway design. It ensures drivers can see far enough ahead to identify and respond to hazards effectively. This directly impacts safety, driver comfort, and the overall efficiency of the transportation network.Vertical curves are classified into crest and sag curves based on their geometry. For crest curves, sight distance is determined by the line of sight between a driver's eye and a small object on the road's surface. Design parameters for...
Design Example: Alignment of a Road Line Using GIS01:17

Design Example: Alignment of a Road Line Using GIS

The alignment of a road line using Geographic Information Systems (GIS) is a critical process in civil engineering, combining advanced technology with practical decision-making. This methodology begins with the collection of geospatial data, including information on land cover, geomorphology, drainage patterns, slope, and contour details. Such data is typically acquired through satellite imagery and GIS tools, offering a comprehensive understanding of the terrain.Once the data is gathered, it...

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

3-O-p-coumaroylquinic acid and 4-O-p-coumaroylquinic acid from Hemerocallis citrina Baroni exert antidepressant effects via CA/Dopa/DA/NE and PKA/CREB/BDNF signaling pathways.

NPJ science of food·2026
Same author

Associations of three metabolic biomarkers with the risk of cardiometabolic multimorbidity: a national prospective study.

Lipids in health and disease·2026
Same author

Root Plasticity and Elemental Stoichiometry Are Associated with Competitive Shifts Between <i>Azolla</i> and <i>Lemna</i> Under Different Nitrogen Levels.

Plants (Basel, Switzerland)·2026
Same author

Coimmobilized Pyridoxal 5'-Phosphate and Aminotransferase in a Hydrogen-Bonded Organic Framework for Sustainable Synthesis of Sitagliptin.

Langmuir : the ACS journal of surfaces and colloids·2026
Same author

<i>q</i> <i>orA</i> shapes organ-specific adaptation of ST59-MRSA via balancing immune evasion and metabolic trade-off.

iScience·2026
Same author

Impact of intraoperative hemostatic material placement on intra-abdominal infection control in acute appendicitis: a retrospective cohort study.

BMC surgery·2026
Same journal

HardFlow: Hard-Constrained Sampling for Flow-Matching Models Via Trajectory Optimization.

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

Industrial Brain: Self-Evolving Neuro-Symbolic Autonomy with Causal Resilience for Cyber-Physical Systems.

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

Adaptive Hardness-Driven Dictionary Distillation for Incomplete Streaming View Clustering.

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

Mixture of Global and Local Experts with Diffusion Transformer for Controllable Face Generation.

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

Task-KV: Task-aware KV Cache Optimization via Semantic Differentiation of Attention Heads.

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

Achieving Text-based Person Retrieval with Any Granularity.

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

相关实验视频

Updated: Jun 21, 2026

Sample Drift Correction Following 4D Confocal Time-lapse Imaging
10:04

Sample Drift Correction Following 4D Confocal Time-lapse Imaging

Published on: April 12, 2014

16.3K

Anchor3DLane++:通过样本适应性稀疏3D回归进行3D车道检测.

Shaofei Huang, Zhenwei Shen, Zehao Huang

    IEEE transactions on pattern analysis and machine intelligence
    |March 3, 2025
    PubMed
    概括
    此摘要是机器生成的。

    这项研究介绍了Anchor3DLane++,这是一种用于单眼3D车道检测的新方法,可以避免鸟眼视图 (BEV) 转换. 它通过直接从前视 (FV) 功能中使用结构3D车道来实现最先进的性能.

    更多相关视频

    Measuring 3D In-vivo Shoulder Kinematics using Biplanar Videoradiography
    06:09

    Measuring 3D In-vivo Shoulder Kinematics using Biplanar Videoradiography

    Published on: March 12, 2021

    3.0K
    Time-Lapse Imaging of Neuronal Arborization using Sparse Adeno-Associated Virus Labeling of Genetically Targeted Retinal Cell Populations
    13:13

    Time-Lapse Imaging of Neuronal Arborization using Sparse Adeno-Associated Virus Labeling of Genetically Targeted Retinal Cell Populations

    Published on: March 19, 2021

    2.8K

    相关实验视频

    Last Updated: Jun 21, 2026

    Sample Drift Correction Following 4D Confocal Time-lapse Imaging
    10:04

    Sample Drift Correction Following 4D Confocal Time-lapse Imaging

    Published on: April 12, 2014

    16.3K
    Measuring 3D In-vivo Shoulder Kinematics using Biplanar Videoradiography
    06:09

    Measuring 3D In-vivo Shoulder Kinematics using Biplanar Videoradiography

    Published on: March 12, 2021

    3.0K
    Time-Lapse Imaging of Neuronal Arborization using Sparse Adeno-Associated Virus Labeling of Genetically Targeted Retinal Cell Populations
    13:13

    Time-Lapse Imaging of Neuronal Arborization using Sparse Adeno-Associated Virus Labeling of Genetically Targeted Retinal Cell Populations

    Published on: March 19, 2021

    2.8K

    科学领域:

    • 计算机视觉 计算机视觉
    • 自主驾驶系统 自主驾驶系统
    • 机器学习 机器学习

    背景情况:

    • 传统的3D车道检测方法依赖于反向透视映射 (IPM) 到鸟眼视图 (BEV),这受到了平面地面假设和信息丢失的影响.
    • 现有的无BEV方法缺乏对3D车道的结构化建模,这与基于BEV的方法相比,阻碍了它们的性能.

    研究的目的:

    • 提出一种新且有效的无BEV单眼3D车道检测方法.
    • 通过引入结构表示和自适应基生成,提高3D车道检测的准确性和稳定性.

    主要方法:

    • Anchor3DLane++使用3D车道作为结构表示,用于从前视图 (FV) 功能直接预测.
    • 一个基于原型的自适应基生成 (PAAG) 模块动态创建稀疏的3D.
    • 对于车道规则化,采用了等宽 (EW) 损失函数,利用它们的并行性质.
    • 通过结合互补的传感器数据,探索相机-LiDAR融合以提高性能.

    主要成果:

    • 与现有的最先进的方法相比,Anchor3DLane++在三个基准数据集上表现出更高的性能.
    • 提出的方法有效地解决了IPM的局限性,并改善了3D信息估计.
    • 整合PAAG和EW损失有助于更准确和更强大的3D车道检测.

    结论:

    • Anchor3DLane++为单眼3D车道检测提供了一个有前途的无BEV方法.
    • 该方法的结构表示和自适应性基生成显著推进了该领域.
    • 未来的工作可以通过传感器融合和精细的损失函数来探索进一步的改进.