DepthFormer: Exploiting Long-Range Correlation and Local Information for Accurate Monocular Depth Estimation
It addresses accurate depth estimation from single images, which is crucial for applications like autonomous driving and robotics, but is incremental as it builds on existing Transformer and CNN methods.
The paper tackles supervised monocular depth estimation by combining Transformer-based long-range correlation modeling with CNN-based local information preservation, achieving state-of-the-art results on benchmarks like KITTI, NYU, and SUN RGB-D with prominent margins.
This paper aims to address the problem of supervised monocular depth estimation. We start with a meticulous pilot study to demonstrate that the long-range correlation is essential for accurate depth estimation. Therefore, we propose to leverage the Transformer to model this global context with an effective attention mechanism. We also adopt an additional convolution branch to preserve the local information as the Transformer lacks the spatial inductive bias in modeling such contents. However, independent branches lead to a shortage of connections between features. To bridge this gap, we design a hierarchical aggregation and heterogeneous interaction module to enhance the Transformer features via element-wise interaction and model the affinity between the Transformer and the CNN features in a set-to-set translation manner. Due to the unbearable memory cost caused by global attention on high-resolution feature maps, we introduce the deformable scheme to reduce the complexity. Extensive experiments on the KITTI, NYU, and SUN RGB-D datasets demonstrate that our proposed model, termed DepthFormer, surpasses state-of-the-art monocular depth estimation methods with prominent margins. Notably, it achieves the most competitive result on the highly competitive KITTI depth estimation benchmark. Our codes and models are available at https://github.com/zhyever/Monocular-Depth-Estimation-Toolbox.