# Learning from Synthetic Shadows for Shadow Detection and Removal

arXiv: https://arxiv.org/abs/2101.01713 · HTML: https://mlsanity.com/paper/2101.01713/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 47 references that are in the map.
- First posted on arXiv 5 Jan 2021.

## Builds on (most cited first, 15 of 15)

- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [AMASS: Archive of Motion Capture as Surface Shapes](https://mlsanity.com/paper/1904.03278/) (2019)
- [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)
- [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)
- [Direction-aware Spatial Context Features for Shadow Detection and Removal](https://mlsanity.com/paper/1805.04635/) (2018)
- [ARGAN: Attentive Recurrent Generative Adversarial Network for Shadow Detection and Removal](https://mlsanity.com/paper/1908.01323/) (2019)
- [Portrait Shadow Manipulation](https://mlsanity.com/paper/2005.08925/) (2020)
- [Instance Shadow Detection](https://mlsanity.com/paper/1911.07034/) (2019)
- [Revisiting Shadow Detection: A New Benchmark Dataset for Complex World](https://mlsanity.com/paper/1911.06998/) (2019)
- [Conditional GANs for Multi-Illuminant Color Constancy: Revolution or Yet Another Approach?](https://mlsanity.com/paper/1811.06604/) (2018)
- [People as Scene Probes](https://mlsanity.com/paper/2007.09209/) (2020)
- [Shadow Optimization from Structured Deep Edge Detection](https://mlsanity.com/paper/1505.01589/) (2015)

## Built on by (most cited first, 27 of 27)

- [ShadowFormer: Global Context Helps Image Shadow Removal](https://mlsanity.com/paper/2302.01650/) (2023)
- [Latent Feature-Guided Diffusion Models for Shadow Removal](https://mlsanity.com/paper/2312.02156/) (2023)
- [Leveraging Inpainting for Single-Image Shadow Removal](https://mlsanity.com/paper/2302.05361/) (2023)
- [Dense Pixel-to-Pixel Harmonization via Continuous Image Representation](https://mlsanity.com/paper/2303.01681/) (2023)
- [Detect Any Shadow: Segment Anything for Video Shadow Detection](https://mlsanity.com/paper/2305.16698/) (2023)
- [Controllable Light Diffusion for Portraits](https://mlsanity.com/paper/2305.04745/) (2023)
- [Single-Image Shadow Removal Using Deep Learning: A Comprehensive Survey](https://mlsanity.com/paper/2407.08865/) (2024)
- [Unveiling Deep Shadows: A Survey and Benchmark on Image and Video Shadow Detection, Removal, and Generation in the Deep Learning Era](https://mlsanity.com/paper/2409.02108/) (2024)
- [When SAM Meets Shadow Detection](https://mlsanity.com/paper/2305.11513/) (2023)
- [Timeline and Boundary Guided Diffusion Network for Video Shadow Detection](https://mlsanity.com/paper/2408.11785/) (2024)
- [AdapterShadow: Adapting Segment Anything Model for Shadow Detection](https://mlsanity.com/paper/2311.08891/) (2023)
- [FieldNet: Efficient Real-Time Shadow Removal for Enhanced Vision in Field Robotics](https://mlsanity.com/paper/2403.08142/) (2024)
- [Benchmarking Shadow Removal for Facial Landmark Detection and Beyond](https://mlsanity.com/paper/2111.13790/) (2021)
- [Structure-Informed Shadow Removal Networks](https://mlsanity.com/paper/2301.03182/) (2023)
- [Learning Physical-Spatio-Temporal Features for Video Shadow Removal](https://mlsanity.com/paper/2303.09370/) (2023)
- [Learning Restoration is Not Enough: Transfering Identical Mapping for Single-Image Shadow Removal](https://mlsanity.com/paper/2305.10640/) (2023)
- [UnShadowNet: Illumination Critic Guided Contrastive Learning For Shadow Removal](https://mlsanity.com/paper/2203.15441/) (2022)
- [Soft-Hard Attention U-Net Model and Benchmark Dataset for Multiscale Image Shadow Removal](https://mlsanity.com/paper/2408.03734/) (2024)
- [RenDetNet: Weakly-supervised Shadow Detection with Shadow Caster Verification](https://mlsanity.com/paper/2408.17143/) (2024)
- [DocShaDiffusion: Diffusion Model in Latent Space for Document Image Shadow Removal](https://mlsanity.com/paper/2507.01422/) (2025)
- [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)
- [SARU: A Shadow-Aware and Removal Unified Framework for Remote Sensing Images with New Benchmarks](https://mlsanity.com/paper/2604.25432/) (2026)
- [Revisiting Shadow Detection from a Vision-Language Perspective](https://mlsanity.com/paper/2605.11771/) (2026)
- [DocPure: Prompt-Free Unified Document Restoration via Degradation-Aware Structure-Guided Wavelet Modulation](https://mlsanity.com/paper/2608.09536/) (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)
- [Shadow Feature Refinement Network: Progressive Feature Refinement based on Knowledge Distillation for Effective Shadow Removal](https://mlsanity.com/paper/2610.05325/) (2026)

## Abstract (arXiv)

Shadow removal is an essential task in computer vision and computer graphics. Recent shadow removal approaches all train convolutional neural networks (CNN) on real paired shadow/shadow-free or shadow/shadow-free/mask image datasets. However, obtaining a large-scale, diverse, and accurate dataset has been a big challenge, and it limits the performance of the learned models on shadow images with unseen shapes/intensities. To overcome this challenge, we present SynShadow, a novel large-scale synthetic shadow/shadow-free/matte image triplets dataset and a pipeline to synthesize it. We extend a physically-grounded shadow illumination model and synthesize a shadow image given an arbitrary combination of a shadow-free image, a matte image, and shadow attenuation parameters. Owing to the diversity, quantity, and quality of SynShadow, we demonstrate that shadow removal models trained on SynShadow perform well in removing shadows with diverse shapes and intensities on some challenging benchmarks. Furthermore, we show that merely fine-tuning from a SynShadow-pre-trained model improves existing shadow detection and removal models. Codes are publicly available at https://github.com/naoto0804/SynShadow.