基于CNN特征提取和加权深度迁移的单目图像深度估计

单目图像的深度估计可以从相似图像及其对应的深度信息中获得。然而，图像匹 配歧义和估计深度的不均匀性问题制约了这类算法的性能。为此，提出了一种基于卷积神经网 络(CNN)特征提取和加权深度迁移的单目图像深度估计算法。首先提取 CNN 特征计算输入图像 在数据集中的近邻图像；然后获得各候选近邻图像和输入图像间的像素级稠密空间形变函数； 再将形变函数迁移至候选深度图像集，同时引入基于 SIFT 的迁移权重 SSW，并通过对加权迁 移后的候选深度图进行优化获得最终的深度信息。实验结果表明，该方法显著降低了估计深度 图的平均误差，改善了深度估计的质量。

Monocular Image Depth Estimation Based on CNN Features Extraction and Weighted Transfer Learning

The depth estimation of monocular image can be obtained from the similar image and its depth information. However, the performance of such an algorithm is limited by image matching ambiguity and uneven depth mapping. This paper proposes a monocular depth estimation algorithm based on convolution neural network (CNN) features extraction and weighted transfer learning. Firstly, CNN features are extracted to collect the neighboring image gallery of the input image. Secondly, pixel-wise dense spatial wrapping functions calculated between the input image and all candidate images are transferred to the candidate depth maps. In addition, the authors have introduced the transferred weight SSW based on SIFT. The final depth image could be obtained by optimizing the integrated weighted transferred candidate depth maps. The experimental results demonstrate that the proposed method can significantly reduce the average error and improve the quality of the depth estimation.