CFDepthNet: Monocular Depth Estimation Introducing Coordinate Attention and Texture Features

Abstract

Handling the depth estimation of low-texture regions using photometric error loss is a challenge due to the difficulty of achieving convergence due to the presence of multiple local minima for pixels in low-texture regions (or even no-texture regions). In this paper, based on the photometric loss, we also introduce texture feature metric loss as a constraint and combine the coordinate attention mechanism to improve the depth map's texture quality and edge detail. This paper uses a simple yet compact network structure, a unique loss function, and a relatively flexible embedded attention module, which is more effective and easier to arrange in robotic platforms with weak arithmetic power. The tests show that our network structure not only shows high quality and state-of-the-art results on the KITTI dataset, but the same training results also perform well on the cityscapes and Make3D datasets.