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

Mesh Analysis01:20

Mesh Analysis

699
Mesh analysis is a valuable method for simplifying circuit analysis using mesh currents as key circuit variables. Unlike nodal analysis, which focuses on determining unknown voltages, mesh analysis applies Kirchhoff's voltage law (KVL) to find unknown currents within a circuit. This method is particularly convenient in reducing the number of simultaneous equations that need to be solved.
A fundamental concept in mesh analysis is the definition of meshes and mesh currents. A mesh is a closed...
699
Modeling and Similitude01:12

Modeling and Similitude

284
Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
284
Shape and Texture of Coarse Aggregate01:25

Shape and Texture of Coarse Aggregate

230
Aggregate shape is classified based on the relative sharpness or roundness of the edges and corners. This classification includes categories like rounded, angular, elongated, and flaky, each with specific characteristics. Rounded aggregates, fully shaped by attrition, are typical of river or seashore gravel, while angular aggregates, such as crushed rock, have well-defined edges. Aggregates that are elongated and flaky are less desirable, as they can reduce the workability and strength of...
230

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

Diverse Semantic Image Editing With Style Codes.

IEEE transactions on neural networks and learning systems·2025
Same author

Refining 3D Human Texture Estimation From a Single Image.

IEEE transactions on pattern analysis and machine intelligence·2024
Same author

Progressive Learning of 3D Reconstruction Network From 2D GAN Data.

IEEE transactions on pattern analysis and machine intelligence·2023
Same author

Benchmarking the Robustness of Instance Segmentation Models.

IEEE transactions on neural networks and learning systems·2023
Same author

Image-to-Image Translation With Disentangled Latent Vectors for Face Editing.

IEEE transactions on pattern analysis and machine intelligence·2023
Same author

Partial Convolution for Padding, Inpainting, and Image Synthesis.

IEEE transactions on pattern analysis and machine intelligence·2022
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 15, 2025

Author Spotlight: Enhancing Skin Model Diversity with Cost-Effective 3D Cellular Models
08:32

Author Spotlight: Enhancing Skin Model Diversity with Cost-Effective 3D Cellular Models

Published on: October 20, 2023

2.7K

用生成模型对3D网格进行精细细节纹理学习.

Aysegul Dundar, Jun Gao, Andrew Tao

    IEEE transactions on pattern analysis and machine intelligence
    |September 26, 2023
    PubMed
    概括
    此摘要是机器生成的。

    本研究介绍了一种新的生成对抗网络 (GAN) 方法,用于从图像中详细的3D模型纹理. 该方法通过改善空间对齐和生成器反来增强纹理学习,从而产生优异的3D纹理模型.

    更多相关视频

    Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands
    10:59

    Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands

    Published on: July 26, 2014

    14.5K
    Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
    09:41

    Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

    Published on: April 21, 2023

    1.6K

    相关实验视频

    Last Updated: Jul 15, 2025

    Author Spotlight: Enhancing Skin Model Diversity with Cost-Effective 3D Cellular Models
    08:32

    Author Spotlight: Enhancing Skin Model Diversity with Cost-Effective 3D Cellular Models

    Published on: October 20, 2023

    2.7K
    Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands
    10:59

    Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands

    Published on: July 26, 2014

    14.5K
    Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
    09:41

    Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

    Published on: April 21, 2023

    1.6K

    科学领域:

    • 计算机视觉 计算机视觉
    • 三维重建的3D重建
    • 机器学习 机器学习

    背景情况:

    • 准确的3D模型重建需要详细的纹理学习.
    • 现有的方法在多视图和单视图图像中的精细纹理细节方面扎.

    研究的目的:

    • 开发一个渐进的框架,用于在3D模型中学习细节纹理.
    • 改进使用注意力和增强区分器反的3D纹理生成学习管道.

    主要方法:

    • 一个两阶段的渐进式学习框架:几何学习,其次是纹理学习.
    • 整合了一种新的注意力机制,用于空间对齐的纹理学习.
    • 通过可学习嵌入来增加区分器输入,以改善发电机反.

    主要成果:

    • 对多视图 (三脚架数据集) 和单视图 (帕斯卡3D+,CUB) 数据集的纹理学习有显著的改进.
    • 与之前的工作相比,在生成高质量的3D纹理模型方面表现出卓越的性能.
    • 验证拟议的注意力机制和可学习的嵌入,以增强纹理生成.

    结论:

    • 拟议的生成学习管道有效地实现了对3D模型的细节纹理学习.
    • 该方法提供了一个强大的解决方案,用于从各种图像输入中重建的3D模型纹理.
    • 这项工作推进了3D纹理模型生成的最先进技术.