CRefNet:使用解码器共享变压器学习一致的反射率估计
IEEE transactions on visualization and computer graphics
|December 1, 2023
概括
本研究介绍了CRefNet,这是一种深度学习模型,用于在内在图像分解中进行一致的反射率估计. CRefNet提高了全球和本地反射一致性,超过了最先进的方法.
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:
- 内在图像分解旨在将图像分成反射率和阴影组件.
- 由于影响材料外观的照明变化,估计一致的反射率是具有挑战性的.
- 现有的方法与全球和本地反射率的一致性作斗争.
研究的目的:
- 开发一个新的深度神经网络,CRefNet,用于准确和一致的反射率估计.
- 在内在图像分解中改善全球和本地反射率的一致性.
- 在内在图像分解方面推进最先进的技术.
主要方法:
- CRefNet采用混合变压器 - 卷积架构.
- 一个新的变压器模块将图像特征转换为反射特征,捕捉远程交互.
- 引入了一个辅助反射率重建任务和一个修正梯度过器.
主要成果:
- 通过其变压器模块,CRefNet实现了增强的全球反射一致性.
- 辅助任务和梯度过器显著提高了反射率图的质量和局部一致性.
- CRefNet的性能比最先进的方法高出10%的WHDR.
结论:
- CRefNet有效地解决了一致的反射率估计的挑战.
- 拟议的方法增强了全球和本地反射一致性.
- CRefNet在内在图像分解方面取得了重大进展.
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