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

2.7K
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.
2.7K

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

Genome-wide identification of the HvSCAMP gene family in barley and functional characterization of the role of HvSCAMP1 in salt tolerance.

BMC plant biology·2026
Same author

HDFLStyler: Hierarchical domain-invariant feature learning for source-free domain generalization.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Common Pattern Prior-Driven Semi-Supervised Medical Image Segmentation.

IEEE transactions on medical imaging·2026
Same author

An optimized haploid breeding strategy employing genome-edited anthocyanin markers for visual selection in tomato.

Plant communications·2025
Same author

Engineering resistance genes against tomato brown rugose fruit virus.

Science China. Life sciences·2025
Same author

Joint Shape Reconstruction and Registration via a Shared Hybrid Diffeomorphic Flow.

IEEE transactions on medical imaging·2025

相关实验视频

Updated: May 6, 2026

Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition
07:45

Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition

Published on: July 21, 2020

5.1K

MonoRelief V2:利用真实数据进行高保真的单眼浮雕恢复

Yu-Wei Zhang, Tongju Han, Lipeng Gao

    IEEE transactions on visualization and computer graphics
    |March 13, 2026
    PubMed
    概括

    MonoRelief V2从单个图像中恢复2.5D浮雕,通过对合成和现实世界的数据进行训练,优于以前的模型. 这种增强的方法提高了2.5D浮雕重建的准确性和效率.

    科学领域:

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

    背景情况:

    • 从单个图像中恢复2.5D浮雕是具有挑战性的,因为复杂的材料和照明变化.
    • 像MonoRelief V1这样的先前模型仅限于在合成数据上进行训练,这影响了现实世界的性能.
    • 需要强大的模型,能够处理各种现实世界的条件.

    研究的目的:

    • 介绍MonoRelief V2,一个改进的端到端模型,用于从单个图像直接恢复2.5D浮雕.
    • 通过将现实世界的数据纳入培训过程,提高模型的稳定性,准确性和效率.
    • 为了展示最先进的深度性能和2.5D浮雕重建的正常预测.

    主要方法:

    • 开发了MonoRelief V2,一个端到端的深度学习模型.
    • 使用文本到图像模型和伪标签的融合深度/正常预测生成了一个大规模的伪真实数据集 (15,000 张图像).
    • 使用多视图重建和改进构建了一个小规模的真实世界数据集 (800个样本).
    • 在伪真实和真实世界数据集上逐步训练MonoRelief V2.

    主要成果:

    • 在深度和正常预测方面,MonoRelief V2 实现了最先进的性能.

    更多相关视频

    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

    2.5K
    Photorealistic Learned Landscapes for Augmented Reality
    06:54

    Photorealistic Learned Landscapes for Augmented Reality

    Published on: June 27, 2025

    845

    相关实验视频

    Last Updated: May 6, 2026

    Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition
    07:45

    Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition

    Published on: July 21, 2020

    5.1K
    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

    2.5K
    Photorealistic Learned Landscapes for Augmented Reality
    06:54

    Photorealistic Learned Landscapes for Augmented Reality

    Published on: June 27, 2025

    845
  • 与其前身相比,该模型表现出更好的稳定性和准确性.
  • 实验验证实训练的有效性,使用伪真实和现实数据的组合.

    • 结论:

    • MonoRelief V2 在单图像2.5D浮雕恢复方面取得了重大进展.
    • 真实数据和先进的数据生成技术的整合增强了模型的概括性.
    • 该模型显示了计算机视觉和图形学的各种下游应用的巨大潜力.