# OmniSR: Shadow Removal under Direct and Indirect Lighting

arXiv: https://arxiv.org/abs/2410.01719 · HTML: https://mlsanity.com/paper/2410.01719/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 58 references that are in the map.
- First posted on arXiv 2 Oct 2024.

## Builds on (most cited first, 30 of 34)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Objaverse: A Universe of Annotated 3D Objects](https://mlsanity.com/paper/2212.08051/) (2022)
- [Uformer: A General U-Shaped Transformer for Image Restoration](https://mlsanity.com/paper/2106.03106/) (2021)
- [3D-FRONT: 3D Furnished Rooms with layOuts and semaNTics](https://mlsanity.com/paper/2011.09127/) (2020)
- [ABO: Dataset and Benchmarks for Real-World 3D Object Understanding](https://mlsanity.com/paper/2110.06199/) (2021)
- [Learning Enriched Features for Real Image Restoration and Enhancement](https://mlsanity.com/paper/2003.06792/) (2020)
- [3D-FUTURE: 3D Furniture shape with TextURE](https://mlsanity.com/paper/2009.09633/) (2020)
- [ATISS: Autoregressive Transformers for Indoor Scene Synthesis](https://mlsanity.com/paper/2110.03675/) (2021)
- [Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal](https://mlsanity.com/paper/1712.02478/) (2017)
- [Towards Ghost-free Shadow Removal via Dual Hierarchical Aggregation Network and Shadow Matting GAN](https://mlsanity.com/paper/1911.08718/) (2019)
- [ShadowDiffusion: When Degradation Prior Meets Diffusion Model for Shadow Removal](https://mlsanity.com/paper/2212.04711/) (2022)
- [Shadow Removal via Shadow Image Decomposition](https://mlsanity.com/paper/1908.08628/) (2019)
- [Mask-ShadowGAN: Learning to Remove Shadows from Unpaired Data](https://mlsanity.com/paper/1903.10683/) (2019)
- [Learning Physics-guided Face Relighting under Directional Light](https://mlsanity.com/paper/1906.03355/) (2019)
- [Auto-Exposure Fusion for Single-Image Shadow Removal](https://mlsanity.com/paper/2103.01255/) (2021)
- [Learning Intrinsic Image Decomposition from Watching the World](https://mlsanity.com/paper/1804.00582/) (2018)
- [DC-ShadowNet: Single-Image Hard and Soft Shadow Removal Using Unsupervised Domain-Classifier Guided Network](https://mlsanity.com/paper/2207.10434/) (2022)
- [Physically-Based Editing of Indoor Scene Lighting from a Single Image](https://mlsanity.com/paper/2205.09343/) (2022)
- [From Shadow Segmentation to Shadow Removal](https://mlsanity.com/paper/2008.00267/) (2020)
- [Direction-aware Spatial Context Features for Shadow Detection and Removal](https://mlsanity.com/paper/1805.04635/) (2018)
- [CANet: A Context-Aware Network for Shadow Removal](https://mlsanity.com/paper/2108.09894/) (2021)
- [A+D Net: Training a Shadow Detector with Adversarial Shadow Attenuation](https://mlsanity.com/paper/1712.01361/) (2017)
- [DeS3: Adaptive Attention-driven Self and Soft Shadow Removal using ViT Similarity](https://mlsanity.com/paper/2211.08089/) (2022)
- [ShadowFormer: Global Context Helps Image Shadow Removal](https://mlsanity.com/paper/2302.01650/) (2023)
- [Materialistic: Selecting Similar Materials in Images](https://mlsanity.com/paper/2305.13291/) (2023)

## Built on by (most cited first, 19 of 19)

- [Detail-Preserving Latent Diffusion for Stable Shadow Removal](https://mlsanity.com/paper/2412.17630/) (2024)
- [DenseSR: Image Shadow Removal as Dense Prediction](https://mlsanity.com/paper/2507.16472/) (2025)
- [ShadowHack: Hacking Shadows via Luminance-Color Divide and Conquer](https://mlsanity.com/paper/2412.02545/) (2024)
- [PhaSR: Generalized Image Shadow Removal with Physically Aligned Priors](https://mlsanity.com/paper/2601.17470/) (2026)
- [Prompt-Aware Controllable Shadow Removal](https://mlsanity.com/paper/2501.15043/) (2025)
- [ShadowMamba: State-Space Model with Boundary-Region Selective Scan for Shadow Removal](https://mlsanity.com/paper/2411.03260/) (2024)
- [NTIRE 2025 Image Shadow Removal Challenge Report](https://mlsanity.com/paper/2506.15524/) (2025)
- [DeshadowMamba: Deshadowing as 1D Sequential Similarity](https://mlsanity.com/paper/2510.24260/) (2025)
- [Contrast-Prior Enhanced Duality for Mask-Free Shadow Removal](https://mlsanity.com/paper/2507.21949/) (2025)
- [D2-Mamba: Dual-Scale Fusion and Dual-Path Scanning with SSMs for Shadow Removal](https://mlsanity.com/paper/2508.12750/) (2025)
- [CANDLE: Illumination-Invariant Semantic Priors for Color Ambient Lighting Normalization](https://mlsanity.com/paper/2604.02785/) (2026)
- [OmniLight: One Model to Rule All Lighting Conditions](https://mlsanity.com/paper/2604.15170/) (2026)
- [Winner of CVPR2026 NTIRE Challenge on Image Shadow Removal: Semantic and Geometric Guidance for Shadow Removal via Cascaded Refinement](https://mlsanity.com/paper/2604.16177/) (2026)
- [CFSR: Geometry-Conditioned Shadow Removal via Physical Disentanglement](https://mlsanity.com/paper/2604.18032/) (2026)
- [WildShadowRemover: In-the-Wild Video Shadow Removal via Detail-Preserving Video Diffusion Models](https://mlsanity.com/paper/2607.26203/) (2026)
- [FreeShadow: Training-Free Shadow Removal via Illumination Transfer and Selective Content Preservation in Diffusion Models](https://mlsanity.com/paper/2607.26715/) (2026)
- [Consistency as Regularization for Unsupervised Shadow Removal](https://mlsanity.com/paper/2609.01806/) (2026)
- [After a Decade: Bringing Shadow Removal into the Real World with Agentic Training Data](https://mlsanity.com/paper/2609.38607/) (2026)
- [ODDR: One-Step Deshadow Diffusion via Reward Guidance](https://mlsanity.com/paper/2610.01291/) (2026)

## Abstract (arXiv)

Shadows can originate from occlusions in both direct and indirect illumination. Although most current shadow removal research focuses on shadows caused by direct illumination, shadows from indirect illumination are often just as pervasive, particularly in indoor scenes. A significant challenge in removing shadows from indirect illumination is obtaining shadow-free images to train the shadow removal network. To overcome this challenge, we propose a novel rendering pipeline for generating shadowed and shadow-free images under direct and indirect illumination, and create a comprehensive synthetic dataset that contains over 30,000 image pairs, covering various object types and lighting conditions. We also propose an innovative shadow removal network that explicitly integrates semantic and geometric priors through concatenation and attention mechanisms. The experiments show that our method outperforms state-of-the-art shadow removal techniques and can effectively generalize to indoor and outdoor scenes under various lighting conditions, enhancing the overall effectiveness and applicability of shadow removal methods.