SVTNet: Dual Branch of Swin Transformer and Vision Transformer for Monocular Depth Estimation

Abstract

In monocular depth estimation, effective acquisition of global and local information is the key to improving accuracy. We introduce a novel dual branch network called Swin Vision Transformer Net (SVTNet), where the Swin Transformer and Vision Transformer are combined to learn features with global and local information. In Swin Transformer branch, we remove the Feature Pyramid Networks which reduces the noise introduced by interpolation and maintains the original characteristics of the hierarchical features extracted from each layer of Swin Transformer. In Vision Transformer Branch, we remove the multi-scale convolutional structure and keep the same-scale feature maps extracted by Vision Transformer for reducing the consumption of memory and extracting more global features. The experimental results on the NYU-Depth v2, KITTI, and CityScapes datasets show that compared with existing advanced methods, the SVTNet has achieved significant performance improvements in multiple evaluation metrics.