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Marigold-DC: Zero-Shot Monocular Depth Completion with Guided Diffusion

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arxiv 2412.13389 v2 pith:HFROCVKF submitted 2024-12-18 cs.CV cs.LG

Massimiliano Viola , Kevin Qu , Nando Metzger , Bingxin Ke , Alexander Becker , Konrad Schindler , Anton Obukhov This is my paper
classification cs.CVcs.LG
keywords depthsparseguidedcompletionmonoculardensediffusionimage
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Depth completion upgrades sparse depth measurements into dense depth maps guided by a conventional image. Existing methods for this highly ill-posed task operate in tightly constrained settings and tend to struggle when applied to images outside the training domain or when the available depth measurements are sparse, irregularly distributed, or of varying density. Inspired by recent advances in monocular depth estimation, we reframe depth completion as an image-conditional depth map generation guided by sparse measurements. Our method, Marigold-DC, builds on a pretrained latent diffusion model for monocular depth estimation and injects the depth observations as test-time guidance via an optimization scheme that runs in tandem with the iterative inference of denoising diffusion. The method exhibits excellent zero-shot generalization across a diverse range of environments and handles even extremely sparse guidance effectively. Our results suggest that contemporary monocular depth priors greatly robustify depth completion: it may be better to view the task as recovering dense depth from (dense) image pixels, guided by sparse depth; rather than as inpainting (sparse) depth, guided by an image. Project website: https://MarigoldDepthCompletion.github.io/

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. PacGDC: Label-Efficient Generalizable Depth Completion with Projection Ambiguity and Consistency

    cs.CV 2025-07 conditional novelty 7.0 of 10

    PacGDC synthesizes diverse pseudo training geometries by rescaling depth predictions from foundation models, improving zero-shot and few-shot generalization of depth completion.

  2. Simulation-based inference using splitting schemes for partially observed diffusions in chemical reaction networks

    stat.ME 2025-08 unverdicted novelty 6.0 of 10

    Chemical Langevin equations are rewritten as perturbed CIR-type SDEs, enabling a structure-preserving splitting scheme and an ABC-SMC algorithm for inference on partially observed reaction networks.

  3. SpatialTrackerV2: 3D Point Tracking Made Easy

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A single feed-forward model jointly estimates video depth, camera poses, and 3D point trajectories from monocular video, setting a new state of the art on TAPVid-3D.

  4. Repurposing Marigold for Zero-Shot Metric Depth Estimation via Defocus Blur Cues

    cs.CV 2025-05 conditional novelty 6.0 of 10

    Two differently blurred images plus a pretrained diffusion depth prior are optimized together at inference time to recover metric depth without retraining.

  5. Non-invasive Assessment of Pancreatic Duct Hypertension Using Computational Flow Modeling

    physics.med-ph 2025-08 unverdicted novelty 5.0 of 10

    A computational model estimates pancreatic duct pressure non-invasively from MRCP geometry, with reported agreement against ERCP pressure measurements.

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