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

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

643
Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
643

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

Structure-aware fatigue modeling in foot deformities: A digital health framework for tissue-specific running injury risk prediction using multi-modal data.

PLOS digital health·2026
Same author

Spatiotemporal inequities in early-life ecological liveability and sleep health in preschool children.

Environmental pollution (Barking, Essex : 1987)·2026
Same author

Understanding the "how" and "why": A mixed methods process evaluation for the PRO-HIIT intervention.

PloS one·2026
Same author

Interlimb differences in knee joint loading and stress distribution following anterior cruciate ligament reconstruction during stair descent.

Clinical biomechanics (Bristol, Avon)·2026
Same author

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

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

[Ultrasound-synergized targeted nanoparticles suppress proliferation, migration and invasion of hypoxic lung cancer cells <i>in vitro</i>].

Nan fang yi ke da xue xue bao = Journal of Southern Medical University·2026

相关实验视频

Updated: Jun 28, 2025

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery
05:12

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery

Published on: August 12, 2021

2.0K

深度学习方法用于校准光度学立体声和超越.

Yakun Ju, Kin-Man Lam, Wuyuan Xie

    IEEE transactions on pattern analysis and machine intelligence
    |April 12, 2024
    PubMed
    概括

    本综述对光度立体的深度学习方法进行了调查,重点关注非兰伯特式表面. 它强调了先进的性能,并建议了表面正常估计的未来研究方向.

    科学领域:

    • 计算机视觉 计算机视觉
    • 这是一种摄影计量技术 (photogrammetry).
    • 机器学习 机器学习

    背景情况:

    • 光度立体声通过使用不同的照明来重建表面的正常值.
    • 非兰伯特反射度使传统方法复杂化.
    • 深度学习在处理复杂的表面特性方面表现有前途.

    研究的目的:

    • 综合审查基于深度学习的校准光度学立体声方法.
    • 基于输入,监督和架构来分析这些方法.
    • 总结业绩并确定未来的研究趋势.

    主要方法:

    • 关于深度学习光度立体技术的文献综述.
    • 对正写摄像机和定向灯的方法分析.
    • 在标准基准数据集上的绩效总结.

    主要成果:

    • 深度学习方法在光度立体声中展示了先进的性能.
    • 分析涵盖了输入处理,监督策略和网络架构.
    • 该审查巩固了当前最先进的深度学习方法.

    结论:

    更多相关视频

    Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools
    09:32

    Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools

    Published on: November 20, 2017

    9.2K
    Determining 3D Flow Fields via Multi-camera Light Field Imaging
    14:25

    Determining 3D Flow Fields via Multi-camera Light Field Imaging

    Published on: March 6, 2013

    16.6K

    相关实验视频

    Last Updated: Jun 28, 2025

    Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery
    05:12

    Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery

    Published on: August 12, 2021

    2.0K
    Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools
    09:32

    Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools

    Published on: November 20, 2017

    9.2K
    Determining 3D Flow Fields via Multi-camera Light Field Imaging
    14:25

    Determining 3D Flow Fields via Multi-camera Light Field Imaging

    Published on: March 6, 2013

    16.6K
  • 深度学习显著增强了光度立体,特别是在非兰伯特表面.
  • 现有的模型表现出强的性能,但有局限性.
  • 未来的研究应该解决这些局限性,以改善表面重建.