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DTV-CNN:基于深度和厚度视图的神经网络,用于高效的3D形状分类.

Qingfeng Xia1

  • 1Culham Centre for Fusion Energy, United Kingdom Atomic Energy Authority, OX14 3DB, United Kingdom.

Heliyon
|November 29, 2023
PubMed
概括

本研究介绍了一种高效的2.5D卷积神经网络 (CNN),用于对3D形状进行深度学习. 该方法显著减少了机械和电子工程设计的培训时间和计算成本.

关键词:
3D形状分类3D形状分类在这里,我们可以看到AIAIAI.在这里,我们可以看到CAD,CAD,CAD.在美国,CNN是CNN.深度地图 - 深度地图一个EDA,一个EDA.智能工程设计 智能工程设计神经网络的神经网络的神经网络厚度 厚度 厚度 厚度

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Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

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.
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