| title | Marigold V2: Revisiting Diffusion Transformers for Monocular Depth Estimation |
|---|---|
| authors | Igor Pavlovic, Thiemo Wandel, Anton Obukhov, Luca Bartolomei, Andrey Davydov, Fabio Tosi, Matteo Poggi, Sabine Süsstrunk, Dengxin Dai |
| date | 2026-09-08 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2609.08084 |
| affiliation | HUAWEI Bayer Lab |
| upvotes | 59 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | Monocular depth estimation is a ubiquitous yet highly ill-posed computer vision task, with downstream applications in scene reconstruction, computational photography, and robotics, among others. Despite the field's maturity, recent models still struggle to generalize to out-of-distribution inputs and to produce sharp and detailed depth maps. In this paper, we revisit Marigold, a set of techniques for repurposing modern image generation and editing models, powered by the diffusion transformer (DiT) architecture, into state-of-the-art monocular depth estimators. Our recipes target single-step inference from pretrained multi-step flow-matching models, with quantization where needed, preserving model capacity while remaining cheap to run. We analyze the artifacts of naive training and identify two effective remedies: aligning the model's internal representations with semantic features extracted from ground-truth, and adopting a 2-stage fine-tuning protocol built around a novel Sinkhorn-based loss. The results are crisper, cleaner depth maps that generalize well out-of-distribution, with 16-26% improvement in AbsRel over the previous best on KITTI and ETH3D. Qualitatively, our model resolves fur, foliage, and hair-thin edges that have eluded prior models. Furthermore, Marigold V2 achieves state-of-the-art results when applied to other dense regression tasks, such as surface normals estimation and intrinsic image decomposition. Project website: https://hf.co/spaces/huawei-bayerlab/marigold-v2-web |
cite
@misc{pavlovic2026marigold,
title={Marigold V2: Revisiting Diffusion Transformers for Monocular Depth Estimation},
author={Igor Pavlovic and Thiemo Wandel and Anton Obukhov and Luca Bartolomei and Andrey Davydov and Fabio Tosi and Matteo Poggi and Sabine Süsstrunk and Dengxin Dai},
year={2026},
eprint={2609.08084},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2609.08084}
}