Depth estimation in light field structured light using SROACC-Net

Depth or disparity estimation plays an important part in computer graphics and computer vision in recent years. Light field imaging has been widely used in the field of depth or disparity estimation because it contains information on light direction and intensity which can provide dense depth estimation. This paper proposes the SROACC-Net for light field structured light disparity estimation based on the OACC-Net with occlusion-aware cost constructor, where squeeze-andexcitation residual net (SE-ResNet) module is added to improve the accuracy. Moreover, Huber-SSIM loss function is designed to boost the performance of the model. The experimental results demonstrate that the SROACC-Net outperforms the OACC-Net in light field structured light depth prediction. The SROACC-Net under light field structured light provides a promising way for depth estimation in computer graphics and computer vision.