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相关概念视频

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

653
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.
653

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相关实验视频

Updated: Jul 4, 2025

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

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基于物理的监督学习方法,用于高动态范围的高保真度3D测量.

Fuqian Li, Xingman Niu, Jing Zhang

    Optics letters
    |February 1, 2024
    PubMed
    概括

    本研究介绍了一种基于物理的深度学习方法,用于高动态范围的3D测量. 这种新的方法改善了网络的融合,并恢复了复杂的表面,具有卓越的细节和概括能力.

    科学领域:

    • 光学和光子学 在光学和光子学.
    • 计算机视觉 计算机视觉
    • 计量学 计量学 计量学

    背景情况:

    • 高动态范围 (HDR) 3D测量至关重要,但具有挑战性.
    • 当前的深度学习方法与复杂的反射性和照明性作斗争,导致融合和稳定性不佳.
    • 现有的无监督方法缺乏复杂表面的细节回收.

    研究的目的:

    • 开发一种强大而准确的HDR 3D测量方法.
    • 改进网络对不同表面特性和照明的融合和通用化.
    • 为了实现精细表面细节的高保真恢复.

    主要方法:

    • 开发了一种基于物理的监督学习方法.
    • 引入了一种新的 sinus-component-to-sinusoidal-component映射范式,其中包含了相位检索的物理模型.
    • 该方法消除了各种照明条件下的边缘强度的尺度差异.

    主要成果:

    • 与传统的监督方法相比,拟议的方法显著提高了网络的融合和泛化.
    • 它在更详细地回收复杂的表面方面优于无监督的方法.
    • 实验证明了高质量的相恢复 (STD误差~0.03rad) 在各种材料 (扩散,金属,混合) 和照明中,验证了优越的概括.

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    Rapid Acquisition of 3D Images Using High-resolution Episcopic Microscopy
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    结论:

    • 基于物理的监督学习方法为HDR 3D测量提供了强大的解决方案.
    • 新的映射范式有效地处理复杂的反射率和照明变化.
    • 该方法在恢复复杂的表面细节和强大的概括能力方面表现出高准确性.