Depth-Versatile Pathways in Imaging

• Depth-versatile pathways are mechanisms that integrate metric and relative depth cues to adapt across varying imaging tasks and camera modalities.
• They employ diverse architectures, including recursive neural modules, canonical transformations, and adapter frameworks, ensuring accurate and efficient depth estimation.
• These systems support zero-shot generalization and robust performance in tasks like completion, super-resolution, and inpainting by fusing multi-modal depth cues.

A depth-versatile pathway describes a methodology, network design, or optical strategy that achieves robust performance across highly variable depth scenarios, camera parameters, or measurement modalities. In computational imaging and computer vision, such pathways unify metric and relative cues, enable adaptability to changing camera intrinsics or sampling schemes, and deliver strong performance on multiple tasks—such as completion, super-resolution, inpainting, or cross-camera generalization—using a single framework. Technically, this spans architectures ranging from recursive neural modules to explicit pixel-wise metric alignment schemes and canonical space mappings.

1. Principles and Definitions

At the core, a depth-versatile pathway is any mechanism by which a depth estimation or imaging system can natively and efficiently handle diversity in:

• Task: depth completion, super-resolution, inpainting, or generic monocular prediction,
• Input modality: sparse or dense priors, metric or relative sources, single- or multi-camera settings,
• Sensor/camera characteristics: varying focal lengths, principal points, intrinsics, or even physical lens structures.

The essential requirement is that the pathway provides high-fidelity depth reconstructions (often in metric scale) across these axes, with minimal or no retraining and at low computational or model-parameter cost. In many instantiations, this includes fusing multiple depth cues, normalizing out source-dependent artifacts, or creating dynamic computation graphs whose depth or calibration varies per input.

2. Methodological Variants

Multiple depth-versatile strategies exist in the recent literature:

a) Metric and Relative Depth Fusion

“Depth Anything with Any Prior” (Wang et al., 15 May 2025) demonstrates a two-stage depth-versatile pathway integrating metric sparse priors and dense relative (monocular) depth. The approach:

1. Coarse Metric Alignment: Fills missing regions of potentially sparse/low-res metric priors by locally solving for affine alignment between the monocular depth prediction and nearby metric measurements using distance-aware weighting:

$$(s, t) = \arg\min_{s, t} \sum_{k=1}^K w_k \big| s D_{\rm pred}(x_k, y_k) + t - D_{\rm prior}(x_k, y_k) \big|^2$$

for each missing pixel using $K$ nearest known prior pixels and $w_k$ decaying with distance.

2. Conditioned Refinement: A shallow network then refines the dense but possibly noisy metric map, conditioned jointly on the prefilled prior, relative prediction, and RGB, thus learning to implicitly fuse geometric and metric cues. The conditioning is realized via side-branch convolutions added to the earliest encoder features.

This design excels in zero-shot generalization across diverse depth completion, super-resolution, and inpainting tasks, with test-time model swapping available for desired efficiency-accuracy tradeoff.

b) Camera-Aware Canonicalization

“Metric3Dv2” (Hu et al., 22 Mar 2024) employs a canonical camera space transformation module as a depth-versatile pathway to generalize metric estimation across thousands of camera models with diverse intrinsics:

• Label or Image Rescaling: All images and/or ground truth depth maps are mapped to a virtual focal length $f^c$ space during training, removing scale ambiguity. At inference, predictions are mapped back with inverse scaling using metadata (intrinsics).
• Joint Depth-Normal Optimization: The deep model is refined iteratively (typically with ConvGRU), yielding both depth and surface normals, robust to cross-camera/cross-domain variability.

This enables a single model to provide correctly scaled metric depths and normals from previously unseen cameras or scene types.

c) Task-Agnostic Generative Pipelines

Probabilistic models, as in (Xia et al., 2019), generate depth samples per-image and allow modular inference for varied downstream tasks (completion, upsampling, guided estimation) without architecture modifications or retraining. Here, the pathway’s versatility comes from outputting the full posterior over depth, enabling conditioning on arbitrary external measurements via energy minimization.

d) Explicit Modularity and Cross-Camera Adaptation

The Versatile Depth Estimator (VDE) (Jun et al., 2023) splits depth estimation into a common relative-depth backbone and lightweight, camera-specific adapters (R2MCs). The pathway is versatile as camera models and scene domains are disentangled: only ≈1% extra parameters are required per new camera, supporting both global and camera-dedicated use cases in a single framework.

3. Network Design and Computational Pathways

Depth-versatile pathways often involve dedicated architectural motifs:

Approach / Paper	Architectural Motif / Module	Explicit Depth-Versatile Component
(Wang et al., 15 May 2025)	Two-stage: pixel-level alignment + MDE	Metric–relative fusion via local affine fill and feature-level conditioning (side-branch encoder integration)
(Hu et al., 22 Mar 2024)	Preprocessing (CSTM) + joint ConvGRU refiner	Canonicalization of camera space; iterative depth-normal distillation
(Xia et al., 2019)	Patchwise conditional VAE + joint MRF	Generative depth samples; application-specific conditioning (energy terms)
(Jun et al., 2023)	Swin Transformer encoder, FMM skip modules, R2MC decoder adapters	Camera-agnostic backbone with modular, lightweight per-camera metric adapters

Technically, these systems realize pathway versatility through orthogonalization of geometric and source-dependent features, dynamic or local affine fitting, explicit modularity for camera parameters, or probabilistic inference at test-time. Pathways may be further endowed with dynamic computation depth or adaptive feature scaling (as in adaptive calibration modules for vision backbones (Guo et al., 11 Jun 2025)).

4. Benchmarks and Empirical Generalization

Crucial to validating a depth-versatile pathway is zero-shot performance across highly diverse settings. Major protocols include:

• Cross-task evaluation: Depth completion from sparse points, super-resolution from low-res maps, inpainting arbitrary shaped holes.
• Cross-camera/cross-domain: Performance on datasets with different camera models (focal length, principal point), scene types (indoor/outdoor), or conflicting priors.
• Metric and Affine-Invariant Depth: Precise scale recovery from unknown or shifted camera settings, contrasted with affine-invariant baselines (Yin et al., 2020).

Key observations include:

• (Wang et al., 15 May 2025): The model achieves state-of-the-art AbsRel on seven real-world datasets across completion, super-res, and inpainting, outperforming task-specific methods.
• (Hu et al., 22 Mar 2024): Metric3Dv2, via canonicalization and joint optimization, delivers high-fidelity metric depths and normals with AbsRel as low as 0.052 (KITTI zero-shot), outperforming per-dataset specialists.
• (Jun et al., 2023): VDE demonstrates near-optimal accuracy both in cross-dataset (mean REL 0.164) and single-camera settings, scaling gracefully to new cameras with negligible parameter increase.

Ablation analyses consistently show that naive interpolation or un-modularized training results in large loss of accuracy or catastrophic calibration drift.

5. Applications Beyond Monocular Estimation

Depth-versatile pathway formulations underpin a wide range of imaging and inference systems:

• 3D Scene Reconstruction: Foundation models with such pathways (as in (Hu et al., 22 Mar 2024)) enable multi-view fusion with metric precision and accurate surface normal layout.
• Sensor Fusion: Fusion of sparse active sensor data (e.g., LiDAR) with dense monocular predictions via pixel-level affine alignment yields practical monocular-metric hybrid systems (Wang et al., 15 May 2025).
• Camera Calibration and Metrology: Canonical-space normalization supports single-image size measurement and scale-drift-free SLAM, critical for robotics and AR/VR (Hu et al., 22 Mar 2024).
• Optical Probes and Endoscopic Imaging: In physical optics, versatile depth-of-field is realized via compound diffractive structures, e.g., stackable Fresnel zone plates and axicons for multi-plane or needle-like focusing (He et al., 25 Jan 2024). Such pathways allow switchable imaging depths, multi-modal sensing, and achromatic or polarization-enhancing stacks unavailable to single-purpose optics.

6. Limitations, Trade-Offs, and Future Directions

While depth-versatile pathways offer strong generalization and practical flexibility, several caveats remain:

• Sensor Metadata Requirement: Explicit camera parameter dependence (canonicalization) requires accurate per-image metadata; robustness to erroneous EXIF/intrinsics is an open challenge.
• Adapter Limitation: Camera-specific modules (e.g., R2MC (Jun et al., 2023)) must still be trained on supervised metric data for each new camera or require meta-learning for rapid adaptation.
• Sample Generation Cost: Probabilistic (VAE-based) approaches are computationally heavier at inference than single-shot models (Xia et al., 2019).
• Noise Propagation: Pixel-level alignment and coarse metric solutions can propagate prior noise, though subsequent refinement networks often mitigate this (Wang et al., 15 May 2025).

Ongoing directions target learnable meta-adapters, self-supervised or few-shot adaptation to new sensor modalities, more robust probabilistic and patch-based sampling, broader fusions with semantic cues, and physical realization in lens and sensor arrays for hybrid imaging systems. The confluence of these strategies is enabling both neural and physical systems with depth versatility as an intrinsic property, rather than application-dependent engineering.