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通过极化成像和深度学习进行三维形状重建

Xianyu Wu¹, Penghao Li¹, Xin Zhang¹

¹School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China.

Sensors (Basel, Switzerland)

|July 11, 2023

概括

这项研究引入了一种新的深度学习方法用于3D成像,该方法可以改善表面正常估计和纹理细节恢复. 先进的极化成像技术在被动照明下提高了3D重建的准确性.

科学领域:

计算机视觉计算机视觉
光学成像技术的成像
深度学习 (Deep Learning) 是一种深度学习.

背景情况:

深度学习的3D成像估计了被动照明下的表面正常.
由于信息丢失,现有的方法在纹理细节和正常准确性方面扎.

研究的目的:

为了增强纹理细节的恢复和3D成像中表面正常估计的准确性.
为了减轻在对象重建期间在精细纹理区域的信息丢失.

主要方法:

使用基于斯托克斯向量的参数和分离的反射元件来优化极化输入.
开发网络以提取全面的极化特征并减少背景噪声.

主要成果:

拟议的方法显著提高了表面正常估计的准确性.
与UNet相比,平均角度误差被证明减少了19%.
实现了62%的计算时间缩短和11%的模型尺寸缩小.

结论:

这种新的方法通过增强极化特征提取来提供更准确的3D重建.
优化极化表示导致在表面正常估计和纹理恢复方面具有卓越的性能.

关键词:

深度学习是一种深度学习.两极化成像成像技术由极化形成的形状.表面正常估计表面正常估计.

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