MonoBooster: Semi-Dense Skip Connection With Cross-Level Attention for Boosting Self-Supervised Monocular Depth Estimation

Accurate depth estimation is crucial for various applications that require precise 3D information about the surrounding environment. In this paper, we propose MonoBooster, a feature aggregation architecture to enhance the performance of self-supervised monocular depth estimation. Specifically, we introduce a semi-dense skip connection scheme to aggregate multi-level features extracted from the backbone network. Additionally, we present a novel Cross-Level Attention (CLA) module to fuse the connected features. The CLA module captures spatial correlation using pyramid depth-wise convolution and adaptively extracts channel information from both low-level and high-level features, facilitating the translation from input RGB image to estimated depth map. Experimental results on the KITTI and Make3D datasets validate the effectiveness of the proposed MonoBooster. Notably, the MonoBooster architecture is flexible and can be seamlessly integrated into popular backbones, resulting in enhanced depth estimation performance.