基于校正遮挡感知的光场深度估计

Depth estimation of light field based on correction occlusion perception

光场图像能够同时记录空间中不同位置和方向的光线信息,为估计精确的深度图提供了丰富的信息。然而在遮挡和重复纹理等复杂场景下,提取图像特征不足会导致深度图的细节丢失。本文提出了一种基于校正卷积的光场深度估计网络,充分利用光场图像丰富的结构信息以改善遮挡等复杂区域的深度估计。利用初始视差图和子孔径图像生成遮挡掩膜,采用校正卷积判别和编码遮挡区域的空间信息以感知遮挡区域,结合多尺度特征以补充易丢失的边缘细节信息。通过空间注意力机制给予遮挡区域更大权重,消除冗余信息并全局优化亚像素代价体。实验结果表明,该方法在4D光场基准平台上的平均MSE和BadPix(ε=0.03)分别达到了0.951和4.261,在大部分场景下能实现最小误差的深度估计,对遮挡区域表现出较高的鲁棒性并优于其他算法。

The light field image can record the light information of different position and direction in space simultaneously,which provides rich information for estimating accurate depth map.However,in complex scenes such as occlusion and repeated texture,the lack of feature extraction will lead to the loss of detail in depth map.An optical field depth estimation network based on correction convolution is proposed to make full use of the rich structural information for optical field images to improve the depth estimation of complex areas such as occlusion.The occlusion mask is generated by using the initial disparity map and subaperture image,and the spatial information of the occlusion area is perceived by correcting convolutional discrimination and encoding,and multi-scale features are combined to supplement the edge details that are easily lost.The spatial attention mechanism is used to give more weight to the occlusion area,eliminate redundant information and optimize the subpixel cost body globally.Experimental results show that average MSE and BadPix(ε=0.03)of the proposed method on 4D optical field reference platform are 0.951 and 4.261,respectively.The proposed method can achieve depth estimation with minimum error in most scenes,and shows high robustness to the occlusion area,which is better than other algorithms.