FuseDepth Combines Frozen AI Visual Priors to Unlock True Metric Depth From a Single Photo

Estimating how far away every pixel in a photograph truly lies, in meters rather than merely in relative order, has long been one of computer vision’s most stubborn challenges. A new open-access study published in Machine Learning with Applications introduces FuseDepth, a framework that pushes zero-shot monocular depth estimation closer to practical deployment by fusing several frozen, off-the-shelf AI models into a single pipeline. Rather than training yet another giant network, the approach borrows the strengths of existing foundation models and composes them in a structured way, achieving sharper boundaries and more reliable absolute scale on scenes the system has never seen.

The central problem the paper identifies is a coupled failure mode. Relative-depth models such as Depth Anything v2 and Marigold produce visually convincing depth maps that generalize well across domains, but their outputs are not metrically calibrated, so converting them to true distances typically requires per-image scale and shift fitting against ground-truth data, which is unavailable in real deployments. Conversely, metric-aware systems such as ZoeDepth, Metric3Dv2, DepthPro, ZeroDepth and UniDepth anchor absolute scale better, yet can blur object boundaries and produce locally inconsistent geometry when the scene distribution shifts. FuseDepth attacks both weaknesses simultaneously instead of trading one for the other.

The architecture works in two stages. In the first, a frozen Depth Anything v2 backbone is adapted with lightweight low-rank adapters, known as LoRA modules, and paired with a boundary-regularized local metric-scale field. Instead of applying a single global affine calibration, a small convolutional head predicts spatially varying scale and shift values, biased toward low-frequency variation and piecewise smoothness. Crucially, the regularization is relaxed near estimated occlusion boundaries, allowing metric scale to change legitimately where objects end, while penalizing arbitrary pixelwise calibration everywhere else. No per-image or per-dataset scale fitting occurs at inference.

The second stage injects explicit high-fidelity visual priors. A pretrained YOLOv8 detector proposes object regions, and each proposal is refined with BiRefNet, an image-matting model that recovers far sharper alpha boundaries than conventional segmentation masks. The gradients of these alpha mattes are aggregated into a scene-wide contour map, and overlapping or adjacent surface hypotheses are linked into a deterministic surface-interaction graph. From this graph, the method derives a soft jump set marking locations where depth may legitimately discontinuously change and where smoothness assumptions should be suspended.

A complementary geometric prior comes from Omnidata, a frozen monocular surface-normal estimator. Surface normals describe the local orientation of planes and curved surfaces, information that is orthogonal to boundary evidence. FuseDepth enforces a projective compatibility between predicted depth gradients and the normal field, computed under the camera’s intrinsic matrix, but this constraint is deliberately suppressed wherever the graph-derived jump set indicates an occlusion boundary. The result is a piecewise regularization strategy: smoothness and normal consistency hold within surfaces, while boundary-preserving correction takes over at discontinuities.

A compact residual network of roughly 0.2 million parameters then refines the coarse metric depth. Rather than simply concatenating inputs, the refiner emits three residual proposals per pixel, one each for contour, normal and coarse-depth corrections, together with softmax-normalized arbitration weights that mix them locally. These learned gates allow the model to favor the contour branch near detected boundaries and lean on normals or the coarse depth in smooth or uncertain regions. The trainable components are learned only through the final depth losses, without any explicit reliability labels.

Evaluation follows a deliberately strict protocol. FuseDepth is trained on roughly 50,000 images from NYUv2, ScanNet and KITTI, and tested zero-shot on three unseen benchmarks spanning panoramic HDR scenes with LiDAR depth, long-range driving footage and diverse indoor and outdoor RGB-D imagery. Relative-depth baselines receive only one affine mapping fitted once on the training mixture and then frozen. Across all targets, FuseDepth posts the lowest errors, reaching an absolute relative error of 0.105 on SYNS, 0.171 on DDAD and 0.182 and 0.276 on the indoor and outdoor DIODE splits, while also recording the lowest per-image log-scale bias, a direct measure of absolute-scale stability.

Mechanism-isolation experiments support the design choices. Removing the contour prior, the normal prior, or the graph structure each degrades accuracy, and replacing the learned gate with plain concatenation or fixed equal weighting is consistently worse. Deliberately corrupted external priors, such as alpha masks taken from the wrong image or normals from a different scene, cause only graceful degradation because the gate shifts its mass away from the corrupted branch. Statistical analysis shows the improvements over the strongest baseline, UniDepth, are significant at the 95 percent level on SYNS and DIODE, while the DDAD advantage is small but reproduced across three independent training seeds and all geographic subsets.

The authors are candid about limitations. The pipeline depends on the quality and cost of its external priors, runs at 15.4 frames per second with 7.4 gigabytes of peak GPU memory, and cannot fully certify that the frozen upstream models never saw the evaluation datasets during their own pretraining. Low-quality contour priors naturally occur on roughly 6 to 12 percent of target images. Even so, the study demonstrates that thoughtfully composing existing foundation models can beat monolithic systems on strict metric transfer, suggesting a future where visual AI advances by orchestrating specialized experts rather than simply scaling up single networks.

Subject of Research: Zero-shot metric monocular depth estimation via fusion of frozen foundation visual priors

Article Title: FuseDepth: Zero-shot metric depth with semantic–geometric fusion of foundation visual priors

Article References: Javidnia, H. (2026). FuseDepth: Zero-shot metric depth with semantic–geometric fusion of foundation visual priors. Machine Learning with Applications, 26, Article 101010. https://doi.org/10.1016/j.mlwa.2026.101010

Image Credits: AI Generated

DOI: 10.1016/j.mlwa.2026.101010

Keywords: monocular depth estimation, zero-shot learning, metric depth, computer vision, foundation models, Depth Anything v2, image matting, surface normals, LoRA, machine learning, depth discontinuities, prior fusion