基于结构重参数化的抗遮挡光场深度估计网络

针对光场深度估计网络结构中运算时间较长的问题，设计了一种能够被重参数化的多分支串联残差块结构（Res-DBLB），加快了网络运算速度，同时引入复合卷积块（RepConv）和卷积注意力模块（CBAM），优化网络性能.对于复杂的遮挡场景，利用深度图生成遮挡掩码，计算遮挡感知成本的构造函数，消除遮挡的负面影响.实验结果表明：与传统方法相比，该算法的均方误差和坏像素率更低，推理速度更快，同时在复杂遮挡场景中表现出较高的稳健性.

Anti-occlusion light field depth estimation network based on structural re-parameterization

A diverse branch residual block (Res-DBLB) which could be re-parameterized was designed to accelerate the network operation speed notably. Meanwhile a RepConv and convolutional block attention module (CBAM) were introduced to optimize the network performance. This was done to address the issue that the optical field depth estimation network structure bringed about long operation time by connecting a large number of multi-branch residual blocks in series to improve the network performance. The depth map was employed to produce occlusion masks and calculate the occlusion-aware cost constructor to eliminate the negative effects of occlusion in complex occlusion situations. The experimental results demonstrated that the method performed more robustly in complicated occlusion settings and had lower mean square error, less bad pixel, and faster inference compared to the conventional method.