CADNet: Context-aggregated DCPPM monocular depth estimation network

Monocular depth estimation is a crucial technology for comprehending scenes, and acquiring global contextual information is pivotal for enhancing depth estimation accuracy. Traditional approaches for incorporating global context information involve pooling feature maps of varying receptive field sizes. Nevertheless, they fail to address challenges such as object boundary distortion and the loss of local detail information caused by complex textures and geometric structures in scenes. To tackle these issues, this paper proposes a novel monocular depth estimation model called CADNet (Context-aggregated DCPPM monocular depth estimation network). This model leverages a multi-scale context aggregation module, DCPPM, to effectively aggregate local features into a global framework, thereby resolving the problem of local detail loss during network training. Experimental results demonstrate that the CADNet model surpasses the NewCRFs model in complex scene boundary detection and capturing local object details. Furthermore, with a 6.27% reduction in parameter count, the CADNet model achieves a noteworthy 9.82% decrease in Sq Rel error on the KITTI dataset and exhibits remarkable performance in general depth estimation metrics for both indoor and outdoor scenes.