Monocular depth estimation with boundary attention mechanism and Shifted Window Adaptive Bins

Abstract

Monocular depth estimation is a classic research topic in computer vision. In recent years, development of Convolutional Neural Networks (CNNs) has facilitated significant breakthroughs in this field. However, there still exist two challenges: (1) The network struggles to effectively fuse edge features in the feature fusion stage, which ultimately results in the loss of structure or boundary distortion of objects in the scene. (2) Classification based studies typically depend on Transformers for global modeling, a process that often introduces substantial computational complexity overhead as described in Equation 2. In this paper, we propose two modules to address the aforementioned issues. The first module is the Boundary Attention Module (BAM), which leverages the attention mechanism to enhance the ability of the network to perceive object boundaries during the feature fusion stage. In addition, to mitigate the computational complexity overhead resulting from predicting adaptive bins, we propose a Shift Window Adaptive Bins (SWAB) module to reduce the amount of computation in global modeling. The proposed method is evaluated on three public datasets, NYU Depth V2, KITTI and SUNRGB-D, and demonstrates state-of-the-art (SOTA) performance.