| dc.description.abstract | Statistical shape modeling is a widely used technique for the representation and analysis of the shapes and shape variations present in a population. A statistical shape model models the distribution in a high dimensional shape space, where each shape is represented by a single point. We present a design study on the intuitive exploration and visualization of shape spaces and shape models. Our approach focuses on the dual-space nature of these spaces. The high-dimensional shape space represents the population, whereas object space represents the shape of the 3D object associated with a point in shape space. A 3D object view provides local details for a single shape. The high dimensional points in shape space are visualized using a 2D scatter plot projection, the axes of which can be manipulated interactively. This results in a dynamic scatter plot, with the further extension that each point is visualized as a small version of the object shape that it represents. We further enhance the population-object duality with a new type of view aimed at shape comparison. This new shape evolution view visualizes shape variability along a single trajectory in shape space, and serves as a link between the two spaces described above. Our three-view exploration concept strongly emphasizes linked interaction between all spaces. Moving the cursor over the scatter plot or evolution views, shapes are dynamically interpolated and shown in the object view. Conversely, camera manipulation in the object view affects the object visualizations in the other views. We present a GPU-accelerated implementation, and show the effectiveness of the three-view approach using a number of realworld cases. In these, we demonstrate how this multi-view approach can be used to visually explore important aspects of a statistical shape model, including specificity, compactness and reconstruction error. | en_US |
