  Single image depth estimation is a foundational task in computer vision and
generative modeling. However, prevailing depth estimation models grapple with
accommodating the increasing resolutions commonplace in today's consumer
cameras and devices. Existing high-resolution strategies show promise, but they
often face limitations, ranging from error propagation to the loss of
high-frequency details. We present PatchFusion, a novel tile-based framework
with three key components to improve the current state of the art: (1) A
patch-wise fusion network that fuses a globally-consistent coarse prediction
with finer, inconsistent tiled predictions via high-level feature guidance, (2)
A Global-to-Local (G2L) module that adds vital context to the fusion network,
discarding the need for patch selection heuristics, and (3) A Consistency-Aware
Training (CAT) and Inference (CAI) approach, emphasizing patch overlap
consistency and thereby eradicating the necessity for post-processing.
Experiments on UnrealStereo4K, MVS-Synth, and Middleburry 2014 demonstrate that
our framework can generate high-resolution depth maps with intricate details.
PatchFusion is independent of the base model for depth estimation. Notably, our
framework built on top of SOTA ZoeDepth brings improvements for a total of
17.3% and 29.4% in terms of the root mean squared error (RMSE) on
UnrealStereo4K and MVS-Synth, respectively.
