The transfer function bake-off

TLDR

Direct volume rendering is essential for visualizing large 3D datasets, and transfer functions—assigning optical properties to data values—are crucial yet notoriously hard to design, making them a major challenge in volume visualization. This article evaluates four leading transfer function design approaches. The approaches examined are trial‑and‑error with minimal computer aid, data‑centric without an underlying model, data‑centric with an underlying data model, and image‑centric using organized sampling.

Abstract

Direct volume rendering is a key technology for visualizing large 3D data sets from scientific or medical applications. Transfer functions are particularly important to the quality of direct volume-rendered images. A transfer function assigns optical properties, such as color and opacity, to original values of the data set being visualized. Unfortunately, finding good transfer functions proves difficult. It is one of the major problems in volume visualization. The article examines four of the currently most promising approaches to transfer function design. The four approaches are: trial and error, with minimum computer aid; data-centric, with no underlying assumed model; data-centric, using an underlying data model; and image-centric, using organized sampling.