Surface reflectance and texture provides a unique signature for applications such as recognition and rendering. Intuition tells us that a camera captures appearance. However, a traditional camera captures intensity dependent on the environment lighting, camera position, and the surface geometry. Imaging for computational appearance recovers reflectance that is intrinsic to an object or scene and useful for recognition and other applications. Reflectance can be captured in a lab-based setting with a gonioreflectometer or domes of lights and cameras. Recent methods in computational imaging provide appearance-capture that is comprehensive, efficient, compact, or optimal depending on the task at hand. In this article, we review methods for capturing and modeling computational appearance. The impact of these appearance representations is significant with applications areas such as e-commerce, digital architecture, humancomputer interaction, intelligent vehicles, robotics, and inspection.