  In the area of self-supervised monocular depth estimation, models that
utilize rich-resource inputs, such as high-resolution and multi-frame inputs,
typically achieve better performance than models that use ordinary single image
input. However, these rich-resource inputs may not always be available,
limiting the applicability of these methods in general scenarios. In this
paper, we propose Rich-resource Prior Depth estimator (RPrDepth), which only
requires single input image during the inference phase but can still produce
highly accurate depth estimations comparable to rich resource based methods.
Specifically, we treat rich-resource data as prior information and extract
features from it as reference features in an offline manner. When estimating
the depth for a single-image image, we search for similar pixels from the
rich-resource features and use them as prior information to estimate the depth.
Experimental results demonstrate that our model outperform other single-image
model and can achieve comparable or even better performance than models with
rich-resource inputs, only using low-resolution single-image input.
