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相关概念视频

Upsampling01:22

Upsampling

266
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
266
Downsampling01:20

Downsampling

194
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
194

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Updated: Jul 26, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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自主监督的任意规模隐性点云采样.

Wenbo Zhao, Xianming Liu, Deming Zhai

    IEEE transactions on pattern analysis and machine intelligence
    |June 22, 2023
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    概括
    此摘要是机器生成的。

    这项研究引入了一种用于点云上采样 (PCU) 的新型自主监督方法,消除了对数据的需求. 该技术实现了放大灵活的PCU,优于现有的监督方法.

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    科学领域:

    • 计算机视觉 计算机视觉
    • 3D数据处理 3D数据处理
    • 机器学习 机器学习

    背景情况:

    • 点云上采样 (PCU) 对于从像LiDAR这样的稀疏传感器输入中生成密集的3D数据至关重要.
    • 对于PCU而言,现有的深度学习方法通常需要广泛的配对数据来进行监督训练,或者由于规模特定的网络而遭受复杂性.

    研究的目的:

    • 开发一种自我监督和放大灵活的方法,用于点云采样.
    • 解决PCU中监督培训和复杂的多层次网络的局限性.

    主要方法:

    • 形成的PCU是为种子点在隐性表面上找到最近的投射点.
    • 定义了用于投影方向和距离估计的两个隐性神经功能,通过借口任务进行训练.
    • 实施了投影校正策略,以消除异常值并保持对象的度.

    主要成果:

    • 拟议的自主监督学习方案与最先进的监督方法相比,实现了竞争力或更高的性能.
    • 证明了PCU隐性神经功能方法的有效性.
    • 验证了该方法在没有明确的地面真相的情况下产生密集和均的点云的能力.

    结论:

    • 新的自我监督PCU方法为数据饥饿的监督方法提供了可行的替代方案.
    • 放大灵活的性质和提高的性能突出显示了这种技术在现实世界应用中的潜力.
    • 这种方法简化了PCU,避免了对不同扩展因子的多个网络的需求.