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arxiv: 2604.16177 · v2 · submitted 2026-04-17 · 💻 cs.CV

Winner of CVPR2026 NTIRE Challenge on Image Shadow Removal: Semantic and Geometric Guidance for Shadow Removal via Cascaded Refinement

Lorenzo Beltrame , Jules Salzinger , Filip Svoboda , Jasmin Lampert , Phillipp Fanta-Jende , Radu Timofte , Marco K\"orner This is my paper

Pith reviewed 2026-05-10 08:40 UTC · model grok-4.3

classification 💻 cs.CV
keywords wsrdchallengentireremovalshadowacrosscvpr2026earlier
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The pith

A three-stage progressive refinement model guided by DINOv2 semantics and geometric depth/normals cues won the NTIRE 2026 image shadow removal challenge with top scores of 26.68 PSNR and 0.874 SSIM.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The method starts with a base image restoration network called OmniSR and turns shadow removal into a three-step process. Each later stage takes the output of the previous one and fixes remaining mistakes such as leftover shadow edges or color shifts. To help the network understand the scene, it adds two kinds of extra information that stay frozen during training: semantic features from the DINOv2 vision model that recognize objects and materials, and geometric signals from off-the-shelf monocular depth and surface normal estimators that describe 3D shape and orientation. A special loss term forces each stage to produce a smaller reconstruction error than the one before it, which keeps training stable. The team trains in phases, first on earlier shadow datasets, then on the new challenge data, and finally combines several checkpoints with cosine annealing to form an ensemble. On the official hidden test set this ensemble reached the highest ranking, and the same model also performed well on two other public shadow datasets.

Core claim

On the official WSRD+ 2026 hidden test set, our final ensemble achieved 26.680 PSNR, 0.8740 SSIM, 0.0578 LPIPS, and 26.135 FID, ranked first overall, and won the NTIRE 2026 Image Shadow Removal Challenge.

Load-bearing premise

That the frozen DINOv2 embeddings and monocular depth/normal estimates supply sufficiently accurate and task-relevant guidance, and that the contraction-constrained objective will reliably stabilize the cascade without capping final performance or causing under-fitting on complex shadows.

Figures

Figures reproduced from arXiv: 2604.16177 by Filip Svoboda, Jasmin Lampert, Jules Salzinger, Lorenzo Beltrame, Marco K\"orner, Phillipp Fanta-Jende, Radu Timofte.

Figure 1
Figure 1. Figure 1: Qualitative comparison on validation samples from [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Proposed three-stage OmniSR pipeline. The shadowed RGB input [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Representative qualitative results on the WSRD+ 2026 [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Stage-wise visualizations on the WSRD+ validation set. [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
read the original abstract

We present a three-stage progressive shadow-removal pipeline for the CVPR2026 NTIRE WSRD+ challenge. Built on OmniSR, our method treats deshadowing as iterative direct refinement, where later stages correct residual artefacts left by earlier predictions. The model combines RGB appearance with frozen DINOv2 semantic guidance and geometric cues from monocular depth and surface normals, reused across all stages. To stabilise multi-stage optimisation, we introduce a contraction-constrained objective that encourages non-increasing reconstruction error across the cascade. A staged training pipeline transfers from earlier WSRD pretraining to WSRD+ supervision and final WSRD+ 2026 adaptation with cosine-annealed checkpoint ensembling. On the official WSRD+ 2026 hidden test set, our final ensemble achieved 26.680 PSNR, 0.8740 SSIM, 0.0578 LPIPS, and 26.135 FID, ranked first overall, and won the NTIRE 2026 Image Shadow Removal Challenge. The strong performance of the proposed model is further validated on the ISTD+ and UAV-SC+ datasets.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 0 invented entities

The claim depends on the transferability of pre-trained vision models and standard deep-learning training assumptions rather than new theoretical derivations.

free parameters (1)
  • contraction constraint strength
    A scalar or weighting term in the multi-stage loss that enforces non-increasing error; its exact value is not stated in the abstract.
axioms (2)
  • domain assumption Frozen DINOv2 features supply useful semantic context for shadow removal
    The method re-uses DINOv2 without adaptation, presupposing that its ImageNet-era representations remain informative for deshadowing.
  • domain assumption Monocular depth and normal estimates are sufficiently accurate to guide shadow removal
    Off-the-shelf estimators are treated as reliable geometric oracles without reported error analysis or fine-tuning.

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Reference graph

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