Intrinsic image decomposition is a long-standing, highly challenging computer vision
problem, where ground truth data is very difficult to acquire. We explore the idea of using …

Image synthesis designed for machine learning applications provides the means to
efficiently generate large quantities of training data while controlling the generation process …

Separating an image into reflectance and shading layers poses a challenge for learning
approaches because no large corpus of precise and realistic ground truth decompositions …

Understanding shading effects in images is critical for a variety of vision and graphics
problems, including intrinsic image decomposition, shadow removal, image relighting, and …

Intrinsic images are a mid‐level representation of an image that decompose the image into
reflectance and illumination layers. The reflectance layer captures the color/texture of …

The amount of hazardous debris in Earth orbit has been increasing, posing an ever-greater
danger to space assets and crewed missions. In January of 2007, a Chinese ASAT test …

SmartShadow is a deep learning application for digital painting artists to draw shadows on
line drawings, with three proposed tools.(1) Shadow brush: artists can draw scribbles to …

A new multi‐illuminant synthetic image test set called MIST is described and made publicly
available. MIST is intended primarily for evaluating illumination estimation and color …

In this paper, we provide a new, real photo dataset with precise ground-truth for intrinsic
image research. Prior ground-truth datasets have been restricted to rather simple …

Deep learning allows computers to learn from observations, or else training data. Successful
application development requires skills in neural network design, adequate computational …