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相关概念视频

Computed Tomography01:10

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
Virtual Work for a System of Connected Rigid Bodies01:06

Virtual Work for a System of Connected Rigid Bodies

Virtual work is a powerful method used to solve problems involving several connected rigid bodies. When the system is in equilibrium, virtual work is zero. This allows the calculation of the resulting forces when a system undergoes a virtual displacement. When attempting to analyze such a system, first, use a free-body diagram, where an independent coordinate represents the configuration of the links, and mark its deflected position resulting from the positive virtual displacement.
Next,...
Convolution Properties II01:17

Convolution Properties II

The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
Deconvolution01:20

Deconvolution

Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...

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相关实验视频

Updated: Jul 13, 2026

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells
12:49

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells

Published on: September 28, 2019

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轻量级的3D卷积占用网络用于虚拟对象重建.

Claudia Melis Tonti, Lorenzo Papa, Irene Amerini

    IEEE computer graphics and applications
    |February 6, 2024
    PubMed
    概括

    本研究介绍了一种使用卷积占用网络的轻量级隐式表示,用于在边缘设备上高效的3D对象重建. 该方法平衡了网格质量和生成速度,在各种硬件上证明有效.

    科学领域:

    • 计算机视觉 计算机视觉
    • 计算机图形 计算机图形
    • 几何深度学习 几何深度学习

    背景情况:

    • 边缘设备需要适应性技术来完成诸如3D对象重建等苛刻任务.
    • 现有的方法往往优先考虑网格质量而不是计算速度,限制了它们在嵌入式系统上的使用.

    研究的目的:

    • 开发一种轻量级的隐性表示,用于高效的3D对象重建.
    • 为了实现嵌入式应用的网格生成时间和网格质量之间的平衡.

    主要方法:

    • 利用卷积占用网络进行轻量级隐式表示.
    • 在ShapeNet数据集上实施和测试该方法.

    主要成果:

    • 在各种硬件上展示了拟议方法的有效性,包括GPU,CPU和嵌入式设备.
    • 在网格生成速度和质量之间实现了有利的平衡.

    结论:

    • 拟议的卷积占用网络方法为在资源受限的边缘设备上进行3D对象重建提供了有效的解决方案.
    • 这种方法提升了在非专业化硬件上的增强现实和虚拟现实中复杂的计算机图形任务的可行性.

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