Monocular depth estimation is very challenging because clues to the exact
depth are incomplete in a single RGB image. To overcome the limitation, deep
neural networks rely on various visual hints such as size, shade, and texture
extracted from RGB information. However, we observe that if such hints are
overly exploited, the network can be biased on RGB information without
considering the comprehensive view. We propose a novel depth estimation model
named RElative Depth Transformer (RED-T) that uses relative depth as guidance
in self-attention. Specifically, the model assigns high attention weights to
pixels of close depth and low attention weights to pixels of distant depth. As
a result, the features of similar depth can become more likely to each other
and thus less prone to misused visual hints. We show that the proposed model
achieves competitive results in monocular depth estimation benchmarks and is
less biased to RGB information. In addition, we propose a novel monocular depth
estimation benchmark that limits the observable depth range during training in
order to evaluate the robustness of the model for unseen depths.

提出了一种名为 RElative Depth Transformer（RED-T）的深度估计模型，使用相对深度作为自注意力的指导，通过分配高关注权重来使类似深度的特征更加相似而不易被误用的视觉线索来提高深度估计的准确性，并在数据集上取得了竞争性的结果。