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相关概念视频

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

550
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.
550

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相关实验视频

Updated: Jun 4, 2025

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery
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通过MAP估计增强基本真相差异,用于开发基于神经网络的立体相机.

Hanbit Gil1, Sehyun Ryu1, Sungmin Woo1

  • 1Department of Information and Communication Engineering, Korea University of Technology and Education (KOREATECH), Cheonan-si 31253, Republic of Korea.

Sensors (Basel, Switzerland)
|December 17, 2024
PubMed
概括

这项研究使用后期最大值 (MAP) 估计增强了半全球匹配 (SGM) 差异地图. 改进的地图提高了神经网络的性能,用于自动驾驶系统的深度传感.

关键词:
在MAP估计中,MAP的估计值.半全球匹配的匹配方法自动驾驶自动驾驶的自动驾驶.深度学习是一种深度学习.不平等地图的地图.插值的插值是指一个插值.神经网络的神经网络的神经网络立体视觉视觉的立体视觉

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Measuring Sensitivity to Viewpoint Change with and without Stereoscopic Cues
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科学领域:

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

背景情况:

  • 半全球匹配 (SGM) 提供了准确的差异地图,但与遮蔽和无纹理区域作斗争.
  • 神经网络提供了视觉上有吸引力的深度估计,但面临着泛化问题.
  • 准确的地面真相差异地图对于训练深度传感系统至关重要.

研究的目的:

  • 为了增强由SGM生成的地面真相差异地图.
  • 为了提高SGM差异地图的视觉质量和可用性,用于训练神经网络.
  • 解决SGM在封闭和无质感区域的局限性.

主要方法:

  • 使用后期最大估计 (MAP) 估计来改进SGM差异地图.
  • 采用贝叶斯推理和对周围差异信息的插入来纠正无效像素.
  • 开发了一种新的方法来增强现有的SGM输出.

主要成果:

  • 增强的差异地图保持了SGM在有效地区的准确性.
  • 与标准SGM相比,改善视觉质量和减少无效差异值.
  • 显著提高了基于神经网络的深度估计模型的性能.

结论:

  • 提出的基于MAP的增强方法为改善差异地图准确性提供了一个强大的解决方案.
  • 这种技术增强了SGM用于训练商业深度传感器件和自主应用的实用性.
  • 改进的地图为先进的立体视觉系统提供了可靠的基础.