Evaluating stereoscopic visualization for predictive rendering

The context of this work is predictive rendering; our objective is to previsualize materials based on physical models within computer graphics simulations. In this work we focus on paints constituted of metallic flakes within a dielectric binder. We want to validate a "virtual material workshop" approach, where a user could change the composition and the microstructure of a virtual material, visualize its predicted appearance, and be able to compare it to an actual sample. To do so, our methodology is to start from Scanning Electron Microscopy (SEM) imaging measures on an actual sample that allowed us to characterize two metrics: flake size and flake density. A statistical model based on those measures was then integrated in our spectral rendering engine using raytracing and photon mapping, with an off axis-frustum method to generate stereoscopic images for binocular visualization. Our objective is twofold: 1) perceptually validate our physical model, we evaluate if the virtual metric perceptually corresponds to the real metric of the real samples; 2) evaluate the contribution of virtual reality techniques in the visualization of materials. To do so, we designed a user study comparing photographs of car paint samples and their virtual counterpart based on a design of experiments. The observers evaluated the visual correspondence of different virtual materials generated from microstructures with varying metric values. The results show a perceptual correspondence between real and virtual metrics. This result has a strong impact: it means that for a desired appearance the proposed models correctly predict the microstructure. The second result is that stereoscopy improves the metric correspondence, and the overall appearance score