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Atomic Nuclei: Nuclear Relaxation Processes01:23

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In the absence of an external magnetic field, nuclear spin states are degenerate and randomly oriented. When a magnetic field is applied, the spins begin to precess and orient themselves along (lower energy) or against (higher energy) the direction of the field. At equilibrium, a slight excess population of spins exists in the lower energy state. Because the direction of the magnetic field is fixed as the z-axis,  the precessing magnetic moments are randomly oriented around the z-axis.
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The radiation pressure applied by an electromagnetic wave on a perfectly absorbing surface equals the energy density of the wave. The wave's momentum also gets transferred to the surface when an electromagnetic wave is entirely absorbed by it. The rate at which momentum is transmitted to an absorbing surface perpendicular to the propagation direction equals the force on the surface.
The average value of the rate of momentum transfer divided by the absorbing area represents the average force...
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Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
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零姿势前的NeRF:从未放置和未排序的图像进行递归辐射场重建.

Xinxin Liu, Qi Zhang, Xue Wang

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |February 27, 2026
    PubMed
    概括

    本研究介绍了Zero-Pose-Prior NeRF,这是一种用于从未设置的图像中重建3D场景的新方法,没有先前知识. 它可以在复杂,无序的数据集中实现高保真视图合成,克服现有的神经辐射场技术的局限性.

    科学领域:

    • 计算机视觉 计算机视觉
    • 计算机图形 计算机图形
    • 机器学习 机器学习

    背景情况:

    • 神经辐射场 (NeRF) 需要精确的摄像头姿势来重建场景.
    • 现有的无姿势NeRF方法难以处理复杂的场景和大量的摄像机动作.
    • 对于当前的无位方法,往往需要先前的知识或合理的初始化.

    研究的目的:

    • 开发一种新的方法来从未设置和未排序的图像集合中恢复辐射场,而无需事先的知识.
    • 为了解决摄像机在NeRF应用程序中构成依赖的关键障碍.
    • 从具有挑战性的数据集中实现强大的场景重建和高保真性视图合成.

    主要方法:

    • 提出了Zero-Pose-Prior NeRF,将问题分解为自我启动的子问题.
    • 实现了层次结构和本地到全球的注册顺序的场景分区.
    • 设计了条件解的位置编码,用于姿势估计和场景表示.
    • 开发了递归注册来估计局部姿势,并将它们统一成一个全球姿势空间.

    主要成果:

    • 在没有任何事先知识的情况下实现了准确的摄像头姿势估计.
    • 从无定位和无序图像中证明了强大的辐射场重建.

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  • 在真实世界的场景上的实验中超越了最先进的无姿势方法.
  • 启用了高保真视图合成,展示了改进的场景表示.
  • 结论:

    • 零姿势前的NeRF有效地从未摆设的图像集合中重建辐射场.
    • 拟议的方法克服了现有的NeRF技术在复杂场景中的局限性.
    • 这种方法显著提升了NeRF在现实世界应用的潜力,而无需提出先决意见.