  This paper introduces PatchRefiner, an advanced framework for metric single
image depth estimation aimed at high-resolution real-domain inputs. While depth
estimation is crucial for applications such as autonomous driving, 3D
generative modeling, and 3D reconstruction, achieving accurate high-resolution
depth in real-world scenarios is challenging due to the constraints of existing
architectures and the scarcity of detailed real-world depth data. PatchRefiner
adopts a tile-based methodology, reconceptualizing high-resolution depth
estimation as a refinement process, which results in notable performance
enhancements. Utilizing a pseudo-labeling strategy that leverages synthetic
data, PatchRefiner incorporates a Detail and Scale Disentangling (DSD) loss to
enhance detail capture while maintaining scale accuracy, thus facilitating the
effective transfer of knowledge from synthetic to real-world data. Our
extensive evaluations demonstrate PatchRefiner's superior performance,
significantly outperforming existing benchmarks on the Unreal4KStereo dataset
by 18.1% in terms of the root mean squared error (RMSE) and showing marked
improvements in detail accuracy and consistent scale estimation on diverse
real-world datasets like CityScape, ScanNet++, and ETH3D.
