Abstract

Using interactive perception to optimise the visual quality of approximated objects in computer graphics.

In real-time computer graphics applications, such as Virtual Reality (VR), three-dimensional objects are often represented as meshes of triangles. Real-time frame rates limit the number of triangles the graphics engine is able to display per frame. Therefore, meshes with a high number of triangles often have to be replaced by meshes with a lower number in order to achieve acceptable display rates; i.e. the Level Of Detail (LOD) needs to be reduced. Several techniques have been developed to derive meshes of lower LOD from one with a high resolution. These approaches are based on the mathematical or logical properties of the original mesh. The results differ in visual quality depending on the strategy used. Visual quality can be described in terms of the probability of the degradation being recognised by the viewer. None of the methods presented so far take the viewer's perception into account.

We present a new, interest-dependent strategy for decreasing the LOD of a given object. The most interesting regions (i.e. triangle clusters) of the object are retained at high resolution for as long as possible, while less interesting regions are coarsened earlier. Thus, the reduction in accuracy should be less recognisable by the viewer. A viewer's interest in a triangle cannot be determined a priori. Hence the object in question is shown to a test viewer, while simultaneously their eye-movements are tracked. We associate a counter with each triangle to measure the frequency with which they fixate it. The number of fixations is then interpreted as a measure for the test viewer's interest in a triangle, and the order with which regions are coarsened is updated accordingly. In other words, the lower LOD meshes get moulded interactively by the test viewer's interest. Meshes created in this way showed significant improvements over meshes created by more traditional methods, even when the LOD was significantly reduced. As yet, this technique has been tested only on the authors, but results were very promising and it is intended to carry out more extensive tests with multiple subjects.