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.

PatchRefiner 是一个高级框架，用于处理高分辨率实际领域输入的度量单图深度估计。通过使用瓷砖分割方法，采用了细节与尺度解耦的损失函数并结合合成数据，PatchRefiner 在深度估计方面取得了显著的性能提升，大幅超过了现有基准，在真实世界数据集上展示了细节准确性和尺度估计的改进效果。