Effects of illumination, texture, and motion on task performance in 3D tensor-field streamtube visualizations

We present results from a user study of task performance on streamtube visualizations, such as those used in three-dimensional (3D) vector and tensor field visualizations. This study used a tensor field sampled from a full-brain diffusion tensor magnetic resonance imaging (DTI) dataset. The independent variables include illumination model (global illumination and OpenGL-style local illumination), texture (with and without), motion (with and without), and task. The three spatial analysis tasks are: (1) a depth-judgment task: determining which of two marked tubes is closer to the user´s viewpoint, (2) a visual-tracing task: marking the endpoint of a tube, and (3) a contact-judgment task: analyzing tube-sphere penetration. Our results indicate that global illumination did not improve task completion time for the tasks we measured. Global illumination reduced the errors in participants´ answers over local OpenGLstyle rendering for the visual-tracing task only when motion was present. Motion contributed to spatial understanding for all tasks, but at the cost of longer task completion time. A high-frequency texture pattern led to longer task completion times and higher error rates. These results can help in the design of lighting model, such as flow or diffusion-tensor field visualizations and identify situations when the lighting is more efficient and accurate.