红外单眼深度估计基于辐射场梯度指导和在HSV空间中的语义priors
Rihua Hao1, Chao Xu1, Chonghao Zhong1
1Key Laboratory of Photoelectronic Imaging Technology and System, Ministry of Education of China, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
这项研究引入了一个基于红外线的单眼深度估计 (MDE) 框架,克服了各种照明中的RGB限制. 这种新的方法通过整合辐射场梯度指导和语义先验来实现高精度.
科学领域:
- 计算机视觉 计算机视觉
- 计算成像技术的成像
- 红外成像技术 红外成像技术
背景情况:
- 使用RGB图像的单眼深度估计 (MDE) 难以应对照明变化.
- 红外图像提供了照明不变的特性,有利于深度估计.
研究的目的:
- 开发一个端到端的框架,用于使用红外图像准确的MDE.
- 加强结构性学习和边缘意识机制,以改善深度预测.
主要方法:
- 一个多任务的UNet架构用于渐变提取,语义细分和从红外线RAW图像的纹理重建.
- 整合了一个辐射场梯度指导 (RGG) 模块,用于边缘意识的注意.
- 将图像特征映射到HSV颜色空间通道 (S,H,V) 并转换为深度网络的RGB.
- 引入一个天空面罩损失来处理模两可的天空区域.
主要成果:
- 在定制红外数据集上的实验验证显示了高精度.
- 获得了delta-1 (δ1) 准确度得分为0.976.976.
- 拟议的方法显著提高了红外图像的深度估计性能.
结论:
- 利用红外图像和HSV颜色空间提高了MDE对照明变化的稳定性.
- 辐射场梯度指导和语义先验对于从红外数据准确估计深度至关重要.
- 开发的框架为MDE在具有挑战性的照明条件下提供了一个有希望的解决方案.
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