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相关概念视频

Random Sampling Method01:09

Random Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
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A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
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A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
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Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
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 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
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    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:

    • 新视图合成对于身临其境的体验至关重要,但通常在计算上昂贵.
    • 由于几何学预计算或神经染中的详尽采样,现有的方法在效率方面扎.

    研究的目的:

    • 从稀疏的输入视图开发出一种快速和实用的解决方案,用于从稀疏的输入视图中合成新的视图.
    • 通过减少采样点来提高神经体积染的效率.

    主要方法:

    • 将学习的多视图立体声 (MVS) 前置纳入神经体积染.
    • 使用基于深度分布的概率导向抽样来减少冗余点.
    • 为不确定的和封闭的地区开发一种信任意识的精细化.

    主要成果:

    • 与最先进的方法相比,实现了15到40倍更快的染速度.
    • 在各种现实世界的场景中表现出强大的概括能力.
    • 在单个GTX 3090 GPU上以~20fps的速度染了512x512的新视图.

    结论:

    • 拟议的方法为新视图合成提供了显著的加速.
    • 这种方法保持了高质量的合成性能,并且很好地泛化.
    • 实现实时自由视角体验和全息显示应用程序.