For a long time, computer graphics has privileged photo-realistic rendering where the challenge lies in producing an image which can not be distinguished from a photograph. However, the realism of the rendering does not provide any guarantee of legibility, that is, the images are not always easy-to-understand. An alternative approach, named Non-Photorealistic Rendering (NPR) has emerging these recent years. Most NPR techniques consists in the proposal of novel algorithms based on traditional artistic techniques (watercolor, pen and ink illustration, sketching, ...). An important issue in NPR is depiction, that is, the extraction of characteristic features from 3D complex data. In this project, we are interested in using NPR for the interactive and legible rendering of complex 3D scenes. The complexity is related to the massive amount of data and to the intrinsic nature of information (images, texts, illustrations...). Many examples in scientific visualization prove that photorealism does not always offer meaningful images. For example in medical books, the representation of organs is more frequently based on sketches and annotations than on photographs. Some various illustration examples are shown below.
Multi-levels view of a sheep. Annotated medical illustration of an hand. Perceptually effective scientific visualization, SIGGRAPH 2002 course
State of the art rendering techniques do not offer suited tools for perceptually effective, legible rendering of complex data. Researchers in the ARTIS and IPARLA projects are working on these topics, in the context of archeology, museography, medical simulation, all applications that require specialized visualizations. To address the legibility question we will rely (i) on the skills and experience of the IPARLA and ARTIS teams; (ii) on the knowledge of specific users: archeologists, museum curators, industrial users at Digisens (the startup specialized in CTscan reconstruction and dental chirurgy); (iii) on the skills of the Bordeaux lab of cognitive psychology who will help usto define the type of validation needed for our methods.
In the rest of this document, we present our research program based on three inter-related main topics: