School of Information and Communication Engineering, Chungbuk National University, Cheongju-si 28644, Chungcheongbuk-do, Korea.
VL2 Center, Gallaudet University, 800 Florida Avenue NE, Washington, DC 20002, USA.
Sensors (Basel). 2022 Jul 15;22(14):5288. doi: 10.3390/s22145288.
This study proposes a robust depth map framework based on a convolutional neural network (CNN) to calculate disparities using multi-direction epipolar plane images (EPIs). A combination of three-dimensional (3D) and two-dimensional (2D) CNN-based deep learning networks is used to extract the features from each input stream separately. The 3D convolutional blocks are adapted according to the disparity of different directions of epipolar images, and 2D-CNNs are employed to minimize data loss. Finally, the multi-stream networks are merged to restore the depth information. A fully convolutional approach is scalable, which can handle any size of input and is less prone to overfitting. However, there is some noise in the direction of the edge. A weighted median filtering (WMF) is used to acquire the boundary information and improve the accuracy of the results to overcome this issue. Experimental results indicate that the suggested deep learning network architecture outperforms other architectures in terms of depth estimation accuracy.
本研究提出了一种基于卷积神经网络（CNN）的鲁棒深度图框架，利用多方向视差平面图像（EPI）计算视差。采用基于三维（3D）和二维（2D）CNN 的深度学习网络的组合，分别从每个输入流中提取特征。3D 卷积块根据视差的不同方向进行调整，2D-CNN 用于最小化数据丢失。最后，多流网络合并以恢复深度信息。全卷积方法具有可扩展性，可处理任何大小的输入，并且不易发生过拟合。但是，在边缘方向存在一些噪声。使用加权中值滤波（WMF）获取边界信息并提高结果的准确性，以克服此问题。实验结果表明，所提出的深度学习网络架构在深度估计准确性方面优于其他架构。
