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相关概念视频

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

602
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.
602
Gestalt Principles of Perception01:21

Gestalt Principles of Perception

281
Gestalt principles provide a framework for understanding how humans perceive objects as unified wholes within their context. These principles are essential in explaining the cognitive processes that make sense of complex visual stimuli by organizing them into coherent groups. One fundamental principle is proximity, which posits that objects located close to each other are perceived as a collective group. For instance, when dots are positioned near one another, the visual system interprets them...
281
Scaling01:26

Scaling

234
In designing and analyzing filters, resonant circuits, or circuit analysis at large, working with standard element values like 1 ohm, 1 henry, or 1 farad can be convenient before scaling these values to more realistic figures. This approach is widely utilized by not employing realistic element values in numerous examples and problems; it simplifies mastering circuit analysis through convenient component values. The complexity of calculations is thereby reduced, with the understanding that...
234
Differential Leveling01:12

Differential Leveling

142
Differential leveling is a precise method in surveying used to determine the elevation difference between two points. Its primary goal is to establish accurate vertical measurements to create level surfaces or grade lines critical for designing and constructing infrastructures such as roads, bridges, and buildings.The procedure for differential leveling begins with setting up and leveling the instrument at a point where the benchmark can be seen. The level rod is held on the benchmark (BM), and...
142
Design Example: Aggregate Gradation01:24

Design Example: Aggregate Gradation

91
The right type and quality of aggregates are crucial for concrete as they significantly influence its properties, mix proportions, and cost-effectiveness. If different sources are available for sand, the commonly used fine aggregate in concrete, the selection of sand is primarily based on its gradation.
The grading, or particle-size distribution, of sand is determined using sieve analysis, with standard sizes ranging from 150 μm to 10 mm (ASTM No. 100 sieve to 3⁄8 in. sieve). Sand is...
91
Deconvolution01:20

Deconvolution

138
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...
138

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学习指导隐式深度函数与规模感知特征融合

Yifan Zuo, Yuqi Hu, Yaping Xu

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    此摘要是机器生成的。

    这项研究引入了一个变压器网络,用于以颜色为导向的深度地图超分辨率,在连续尺度上有效地融合色彩和深度特征. 新方法显著提高了深度地图分辨率,使用隐性函数和跨领域的注意力.

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    科学领域:

    • 计算机视觉 计算机视觉
    • 深度学习 (Deep Learning) 是一种深度学习.
    • 图像处理 图像处理

    背景情况:

    • 隐式函数学习对于单个图像超分辨率很受欢迎.
    • 使用隐式函数进行超分辨率的彩色导向深度地图是较少探索的.
    • 关键的挑战包括功能融合,规模信息集成和跨领域注意力建模.

    研究的目的:

    • 调查连续上采样尺度的编码器中融合深度和颜色特征的必要性和适用性.
    • 在编码器和解码器中确定尺度信息的重要性.
    • 开发一种有效的方法来模拟解码器中的跨域特征亲和力.

    主要方法:

    • 一个基于变压器的网络,具有独立的深度超分辨率和引导提取分支.
    • 编码器中的隐性交叉变压器将颜色指导与连续坐标映射融合在一起,并过不相关的指导.
    • 尺度信息嵌入到编码器的位置编码和前网络中,以实现尺度意识的表示.
    • 解码器使用隐式自我注意和交叉注意来重建高分辨率的深度图.

    主要成果:

    • 拟议的网络有效地融合了色彩和深度特征,以实现超分辨率.
    • 将尺度信息集成到编码器中可以增强特征表示.
    • 实验表明,在各种上采样尺度上,合成和真实数据集的性能得到了提高.
    • 该方法在分销和分销之外的规模上都显示出有效性.

    结论:

    • 基于变压器的方法成功地解决了色彩引导深度图超分辨率的挑战.
    • 隐含的功能学习与跨领域的关注相结合,提供了一个强大的框架.
    • 该方法在生成高分辨率深度地图方面取得了重大进展.