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

673
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.
673

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

INR Smooth: Interframe noise relation-based smooth video synthesis on diffusion models.

PloS one·2025
Same author

Context-Fused Guidance for Image Captioning Using Sequence-Level Training.

Computational intelligence and neuroscience·2022
查看所有相关文章

相关实验视频

Updated: Jul 10, 2025

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
11:34

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

15.7K

E2LNet:一个高效和有效的轻量级网络,用于全景深度估计.

Jiayue Xu1, Jianping Zhao1, Hua Li1

  • 1School of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China.

Sensors (Basel, Switzerland)
|November 25, 2023
PubMed
概括
此摘要是机器生成的。

本研究引入了单眼全景深度估计的新框架,解决了扭曲和全球背景问题. 该方法以较少的参数实现了竞争性性能,为移动增强现实提供了可扩展的解决方案.

关键词:
扩张的卷积扩张的卷积.全球平均汇聚的全球平均值全景深度估计估计像素明智的注意力注意力.

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

2.0K
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

9.3K

相关实验视频

Last Updated: Jul 10, 2025

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
11:34

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

15.7K
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

9.3K

科学领域:

  • 计算机视觉 计算机视觉
  • 机器人技术 机器人技术 机器人技术
  • 机器学习 机器学习

背景情况:

  • 单眼全景深度估计对于机器人和自动驾驶至关重要,它可以实现全视野.
  • 现有的方法在全球上下文捕捉和全景扭曲方面扎.
  • 在整个全景中精确的深度感知对于现实应用至关重要.

研究的目的:

  • 开发一种用于单眼全景深度估计的新型框架.
  • 为了同时解决全景扭曲和提取全球上下文信息.
  • 为了提高全景深度估计的性能和可扩展性.

主要方法:

  • 提出了适应性注意力扩展卷积模块,通过适应性调整受感场来感知扭曲.
  • 设计了一个全球场景理解模块,将全球背景集成到功能地图中.
  • 在虚拟和现实世界RGB-D全景数据集上训练和评估模型.

主要成果:

  • 与现有技术相比,拟议的方法实现了具有竞争力的定量和质量性能.
  • 证明了对全球背景的扭曲和整合的有效感知.
  • 该模型显示的参数较少,并且比以前的方法更具灵活性.

结论:

  • 开发的框架为单眼全景深度估计提供了强大的解决方案.
  • 该方法是一种可扩展和灵活的选择,用于移动增强现实等应用程序.
  • 解决了全景深度估计的关键挑战,增强了其实际实用性.