Robust Monocular Depth Estimation Under Crop-Resize-Induced Intrinsics Mismatch

Monocular metric depth estimation models increasingly incorporate camera intrinsics or learn camera-aware representations to recover physically meaningful scales. However, existing camera-aware studies have paid limited attention to a practical deployment gap: common crop, resize, and padding operations alter the image coordinate system, but inference pipelines may still pass stale camera intrinsics to the model. This image–intrinsics inconsistency produces a crop-resize-induced intrinsics mismatch, which can lead to systematic depth bias and degraded geometric consistency. We show that once preprocessing parameters are fixed, the effective intrinsics are determined by a parametric affine mapping, enabling resize-induced focal-length scaling errors and crop-induced principal-point shifts to be analyzed separately. We further distinguish this parameter-conditioned mismatch from broader calibration uncertainty caused by noisy intrinsics, noisy preprocessing metadata, or missing metadata. Based on this formulation, we introduce a controlled evaluation protocol and a lightweight Mismatch-Aware Camera Module (MACM) that combines preprocessing metadata with image-derived camera cues to condition intermediate depth features. In our ablation study, MACM with the proposed consistency loss reduces the mismatched Abs.Rel from 0.141 to 0.114 and narrows the robustness gap from 0.038 to 0.017, while preserving accuracy under matched preprocessing. These results indicate that treating the image and intrinsics as a coupled representation is essential for robust monocular metric depth estimation in practical preprocessing pipelines.