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

612
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.
612

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

EndoLRMGS: Combining Large Reconstruction Modelling and Gaussian Splatting for Complete Endoscopic Scene Reconstruction.

IEEE transactions on medical imaging·2026
Same author

Robust quantification of ICG fluorescence perfusion in neonatal bowel surgery via deep point tracking.

International journal of computer assisted radiology and surgery·2026
Same author

Blob representation of robotic surgical scenes for position-aware video generation.

International journal of computer assisted radiology and surgery·2026
Same author

VerTE-MT: A Multi-Task Framework with Entropy-Guided Sampling for Vertebrae Segmentation and Localisation in CT.

IEEE journal of biomedical and health informatics·2026
Same author

SurgViVQA: temporally grounded video question answering for surgical scene understanding.

International journal of computer assisted radiology and surgery·2026
Same author

A benchmark of methods for surgical instrument segmentation in endoscopic pituitary surgery.

International journal of computer assisted radiology and surgery·2026

相关实验视频

Updated: Jun 16, 2025

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

8.9K

阴影:通过非兰伯特图像分解进行自我监督的单眼深度估计.

Rema Daher1, Francisco Vasconcelos2, Danail Stoyanov2

  • 1Department of Computer Science, UCL Hawkes Institute, University College London, Gower Street, London, WC1E 6BT, UK. rema.daher.20@ucl.ac.uk.

International journal of computer assisted radiology and surgery
|May 13, 2025
PubMed
概括

本研究引入了一种新的自我监督模型 (SHADeS),用于3D结肠镜场景重建. 通过模拟镜面反射,它可以改善深度估计和光分解,帮助导航和多体的特征.

关键词:
单眼的深度是一个单眼深度.自我监督 自我监督镜子中的亮点 镜子中的亮点

更多相关视频

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.7K
Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing
06:25

Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing

Published on: February 23, 2024

556

相关实验视频

Last Updated: Jun 16, 2025

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

8.9K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.7K
Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing
06:25

Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing

Published on: February 23, 2024

556

科学领域:

  • 计算机视觉 计算机视觉
  • 医疗成像医学成像
  • 计算几何学的计算几何学

背景情况:

  • 结肠镜中的3D场景重建有助于导航和息肉分析.
  • 照明变化,特别是镜面反射,带来了重大挑战.
  • 准确的深度和形状表征对于有效的结肠镜检查至关重要.

研究的目的:

  • 调查在结肠镜成像中光线和深度脱的方法.
  • 开发一种对复杂的照明和镜面反射具有强度的模型.
  • 改进3D场景重建,以提高结肠镜导航和多体特征.

主要方法:

  • 推出了一种自主监督的模型,名为SHADeS (阴影,阿尔贝多,深度和光谱).
  • SHADeS同时从单个结肠镜图像中估计阴影,白度,深度和光谱.
  • 采用非兰伯特模型,将镜面反射视为明确的光成分,与之前的方法不同.

主要成果:

  • 证明以前的光分解 (IID) 和深度估计 (MonoViT,ModoDepth2) 模型受到光谱的负面影响.
  • SHADeS成功地产生了强大的光分解和深度图,不受光谱区域的影响.
  • 对幻影数据 (C3VD) 的定量比较进一步验证了该模型的稳定性.

结论:

  • 模拟镜面反射显著提高了结肠镜中深度估计的准确性.
  • 提出的自主监督的SHADeS方法有效地整合了光分解和深度估计.
  • 改进的光分解显示了诸如结肠位置识别等应用的潜力.