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Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

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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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基于相机的单眼深度估计在正牙科:视觉变压器与CNN模型性能对比.

Arda Arısan1, Gökhan Serhat Duran2

  • 1Independent Researcher, Ankara 06000, Turkey.

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概括

使用视觉转换器的单眼深度估计 (MDE) 可以准确地评估2D照片中的面部形状. 这种计算机视觉技术在正牙诊断和治疗规划方面表现有前途.

关键词:
卷积神经网络是一种卷积神经网络.单眼深度估计方法计算机视觉 计算机视觉医学成像医学成像ортодонтическая诊断 诊断 ортодонтическая诊断 诊断 ортодонтическая诊断视觉变压器 视觉变压器

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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
  • 矯正牙科 矯正牙科是一種矯正牙科.

背景情况:

  • 单眼深度估计 (MDE) 从单个2D图像中预测深度.
  • ортодонтика利用面部软组织评估进行诊断和治疗.
  • MDE提供了从标准的正面照片中获取斜形状信息的潜力.

研究的目的:

  • 确定MDE是否可以提取临床上有意义的数据来评估面部形状.
  • 为了评估MDE在正牙应用中的有效性.

主要方法:

  • 追溯分析了82名成年患者的额头照片和脑电图.
  • 上唇前部 (ULA),下唇前部 (LLA) 和软组织Pogonion (Pog') 标志的注释.
  • 将MDE衍生的深度排名与头脑测量真垂直线 (TVL) 分析进行比较.
  • 对DPT-Large (视觉变压器) 和基于CNN的模型 (DepthAnything-v2,ZoeDepth) 的评估.

主要成果:

  • 视觉变压器DPT-Large实现了92.7%的准确性,明显超过了CNN模型.
  • 美国有线电视新闻网的模型DepthAnything-v2 (9.8%) 和ZoeDepth (4.9%) 的表现低于机会水平 (16.7%).
  • 在大多数情况下,DPT-Large表现出临床上可接受的准确性.

结论:

  • 基于视觉变压器的MDE显示出从前部照片中具有临床意义的软组织概况的潜力.
  • 来自二维图像的深度信息可以支持正牙科的面部形状评估.
  • 这些发现为将基于深度的分析整合到数字牙科诊断中奠定了基础.