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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.
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相关实验视频

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Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery
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通过监督深度学习从单眼内镜图像序列进行维护尺度的形状重建.

Takeshi Masuda1, Ryusuke Sagawa1, Ryo Furukawa2

  • 1Artificial Intelligence Research Center National Institute of Advanced Industrial Science and Technology (AIST) Tsukuba Ibaraki Japan.

Healthcare technology letters
|April 19, 2024
PubMed
概括

这项研究引入了一种新的方法,用于从单眼内镜图像中进行3D形状重建. 该方法训练绝对深度预测网络以保持尺寸,从而从内镜视频序列中准确地恢复3D形状.

关键词:
计算机视觉 计算机视觉卷积神经网络是一种卷积神经网络.数据整合数据集成.内镜的内镜是指内镜.虚拟现实 虚拟现实 虚拟现实 虚拟现实

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科学领域:

  • 计算机视觉 计算机视觉
  • 医疗成像医学成像
  • 三维重建的3D重建

背景情况:

  • 从图像中重建3D形状正在获得吸引力.
  • 现有的方法往往会产生具有不确定的尺度的相对深度图.
  • 精确的维护尺寸的3D重建对于各种应用至关重要.

研究的目的:

  • 提出一种用于从单眼内镜图像序列重建维护尺寸的3D形状的新方法.
  • 开发一个绝对深度预测网络,用于准确的尺度估计.
  • 在模拟和真实内镜数据上验证该方法.

主要方法:

  • 使用内镜模拟器生成了一组同步RGB图像和深度图的数据集.
  • 一个受监督的深度预测网络被训练来从RGB图像中估计深度地图,最大限度地减少对地面真实深度的损失.
  • 预测的深度图序列被对齐以重建最终的3D形状.

主要成果:

  • 经过训练的网络成功地从单眼内镜图像中估计了绝对深度图.
  • 预测的深度图的对齐使得能够重建维护尺寸的3D形状.
  • 拟议的方法在应用于真实内镜图像序列时显示出有效性.

结论:

  • 开发的绝对深度预测网络有效地解决了基于单眼内镜的3D重建中的规模模糊性.
  • 这种方法为从内镜图像中创建精确的3D模型提供了一个有前途的方法.
  • 该技术在医学诊断和手术规划方面具有潜在的应用.