# Shading Annotations in the Wild

## TL;DR

This paper introduces SAW, a large-scale dataset of shading annotations in indoor scenes, and demonstrates how training neural networks on this data improves shading understanding and intrinsic image decomposition.

## Contribution

The paper presents a new dataset of shading annotations in the wild and shows how it can be used to train neural networks for better shading prediction.

## Key findings

- Neural networks trained on SAW improve shading predictions.
- SAW dataset reduces artifacts in intrinsic image decomposition.
- Crowdsourced and RGB-D generated annotations complement each other.

## Abstract

Understanding shading effects in images is critical for a variety of vision and graphics problems, including intrinsic image decomposition, shadow removal, image relighting, and inverse rendering. As is the case with other vision tasks, machine learning is a promising approach to understanding shading - but there is little ground truth shading data available for real-world images. We introduce Shading Annotations in the Wild (SAW), a new large-scale, public dataset of shading annotations in indoor scenes, comprised of multiple forms of shading judgments obtained via crowdsourcing, along with shading annotations automatically generated from RGB-D imagery. We use this data to train a convolutional neural network to predict per-pixel shading information in an image. We demonstrate the value of our data and network in an application to intrinsic images, where we can reduce decomposition artifacts produced by existing algorithms. Our database is available at http://opensurfaces.cs.cornell.edu/saw/.