Metric Calibration for Arbitrary Monocular Depth Estimation Models Using Ultra-Sparse Measurements

Existing monocular depth estimation methods recovering dense 3D structure from a single RGB image has achieved remarkable success while still suffers from inherent absolute metric scale ambiguity. Sparse metric-guided methods address this by incorporating metric depth measurements. In practice, however, available measurements are often far sparser than required, especially at long range. We refer to this setting as depth estimation with ultra-sparse metric measurements (USMM). To address this problem, we propose AnchorDepth, a training-free post-processing framework that calibrates monocular depth predictions using USMM. It anchors the predicted depth map to the real-world metric scale without any learnable parameters. AnchorDepth identifies approximately fronto-parallel planes from the output of any pretrained monocular depth model and fits local metric corrections within them. It then propagates the remaining calibration residuals using either bounded inverse-squared-distance interpolation (AnchorDepth-Interp) or edge-aware global optimization (AnchorDepth-Refine). Extensive evaluations on public benchmarks showed that AnchorDepth reduced AbsRel by up to 76% compared with the corresponding uncalibrated monocular depth models. Furthermore, considering that existing benchmarks mainly focus on short-range scenes and provide limited evaluation of long-range depth, we built two benchmarks of kilometer-scale: synthetic STREET, and real-world FAR. On both benchmarks, AnchorDepth reduced AbsRel by approximately 40% compared with state-of-the-art metric-guided methods. These results demonstrated that our method outperformed both sparse metric-guided and RGB-only baselines in the USMM setting.