ONCE: Patch-Free High-Resolution Depth Refinement via One-Pass Calibration and Enhancement

Recent high-resolution metric depth refinement methods often recover fine geometric details through patch-based processing, requiring repeated local inference and subsequent fusion. We present ONCE (One-pass Network for Calibration and Enhancement), a patch-free depth booster that converts the output of a frozen monocular metric-depth backbone into a 4K depth map with a single full-frame refinement pass. Unlike patch-based approaches, ONCE decomposes depth refinement into complementary global and local correction stages. It first performs learned global metric calibration to correct image-dependent scale and shift errors in the coarse prediction. It then combines RGB guidance with the coarse depth representation through a dual-encoder refinement architecture and predicts a dense depth-relative correction in log space, enabling spatially varying geometric details to be recovered while preserving the globally calibrated depth structure. This formulation enables native full-frame refinement without explicit patch extraction, repeated overlapping inference, or subsequent patch fusion, while remaining applicable to different frozen metric-depth backbones. On UnrealStereo4K, ONCE-L (ZoeDepth backbone) achieves δ₁ = 99.052%, RMSE = 0.7839, and SEE = 0.6084, establishing state-of-the-art performance among high-resolution depth refinement methods. Compared with the strongest PatchRefiner V2 variant, ONCE-L achieves higher depth accuracy while reducing the number of added parameters from 245.8M in PRV2-C to 192.9M, corresponding to a 21.5% reduction. Consistent improvements across ZoeDepth and DAv2 backbones further demonstrate that ONCE is not tied to a specific coarse depth estimator. Experiments on Cityscapes also show that the same full-frame refinement formulation transfers to real-world driving images. These results demonstrate that global metric calibration, RGB-guided local refinement, and depth-relative correction can be effectively integrated within a single full-frame pass for accurate and efficient high-resolution metric depth estimation.