  Monocular depth estimation is an ongoing challenge in computer vision. Recent
progress with Transformer models has demonstrated notable advantages over
conventional CNNs in this area. However, there's still a gap in understanding
how these models prioritize different regions in 2D images and how these
regions affect depth estimation performance. To explore the differences between
Transformers and CNNs, we employ a sparse pixel approach to contrastively
analyze the distinctions between the two. Our findings suggest that while
Transformers excel in handling global context and intricate textures, they lag
behind CNNs in preserving depth gradient continuity. To further enhance the
performance of Transformer models in monocular depth estimation, we propose the
Depth Gradient Refinement (DGR) module that refines depth estimation through
high-order differentiation, feature fusion, and recalibration. Additionally, we
leverage optimal transport theory, treating depth maps as spatial probability
distributions, and employ the optimal transport distance as a loss function to
optimize our model. Experimental results demonstrate that models integrated
with the plug-and-play Depth Gradient Refinement (DGR) module and the proposed
loss function enhance performance without increasing complexity and
computational costs on both outdoor KITTI and indoor NYU-Depth-v2 datasets.
This research not only offers fresh insights into the distinctions between
Transformers and CNNs in depth estimation but also paves the way for novel
depth estimation methodologies.
