Existing stereoscopic 3D (S3D) salient object detection (SOD) networks typically employ a two-branch architecture, in which the RGB and depth channels are learned independently. Conventional methods based on conventional neural networks generally fuse the two branches by combining their deep representations at a later stage with only one path, which can be inefficient and insufficient for retaining a large amount of cross-modal data. In this study, we combine the RGB branch and depth branch to generate a third branch. The first branch is the embedded attention branch containing the attention mechanism, and we introduce the embedded attention module in this branch to give the allocation of available processing resources to the most informative components of an input signal. The second branch is the boundary refinement branch combined with the low-level information of RGB and depth images. Additionally, we propose a new module, called the detail correlation module, to ensure clear object boundaries and salient object refinement. The third branch is the global deep-view branch containing the global view module, which fuses high-level information and expands the sensor field. We also use three different loss functions to match our special SOD network. Extensive experiments demonstrate the effectiveness and robustness of the proposed architecture and show that it represents a significant improvement over other state-of-the-art SOD approaches.

现有的立体3D（S3D）显着物体检测（SOD）网络通常采用两分支架构，其中RGB和深度通道是独立学习的。基于常规神经网络的常规方法通常通过将后期的深度表示与仅一条路径组合在一起来融合这两个分支，这可能效率不高且不足以保留大量的交叉模态数据。在这项研究中，我们结合了RGB分支和深度分支来生成第三分支。第一个分支是包含注意机制的嵌入式注意分支，我们在此分支中引入嵌入式注意模块，以将可用处理资源分配给输入信号中最有用的部分。第二个分支是边界细化分支，结合了RGB和深度图像的低级信息。此外，我们提出了一个新的模块，称为细节相关模块，以确保明确的对象边界和显着的对象细化。第三个分支是包含全局视图模块的全局深度视图分支，该模块融合了高级信息并扩展了传感器范围。我们还使用三种不同的损失函数来匹配我们的特殊SOD网络。大量的实验证明了所提出体系结构的有效性和鲁棒性，并表明它相对于其他最新的SOD方法具有显着改进。第三个分支是包含全局视图模块的全局深度视图分支，该模块融合了高级信息并扩展了传感器范围。我们还使用三种不同的损失函数来匹配我们的特殊SOD网络。大量的实验证明了所提出体系结构的有效性和鲁棒性，并表明它相对于其他最新的SOD方法具有显着改进。第三个分支是包含全局视图模块的全局深度视图分支，该模块融合了高级信息并扩展了传感器范围。我们还使用三种不同的损失函数来匹配我们的特殊SOD网络。大量的实验证明了所提出体系结构的有效性和鲁棒性，并表明它相对于其他最新的SOD方法具有显着改进。