  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.
