The human visual system perceives 3D depth by observing how much points shift between the left eye and right eye. Toon-shaded renders of 3D models often have untextured surfaces with discretized shading, and these flat featureless areas give the eyes few reference points to perceive 3D depth. The primary depth cues come from contour lines and the discrete shading lines dividing bright and dark, so they have to be used to their fullest. This research tries to develop a method for automatically optimizing depth-perception for a 3D toon-shaded scene with smart light placement, local editing of shading, and the generation of effective contour lines.

Ambient occlusion is a popular rendering technique that creates a greater sense of depth and realism, by darkening places in the scene that are less exposed to ambient light (e.g., corners and creases). Ambient occlusion measures how geometrically occluded each point in the scene is and modulates the ambient light accordingly. In real-time applications, screen-space ambient occlusion approximations are used, due to their great performance and visual quality. However, these screen-space approximations only take the geometry currently visible on screen into account. This results in underestimated or missing ambient occlusion when geometry is hidden from view (e.g., hidden behind other geometry). Our proposal, stochastic-depth ambient occlusion, improves upon traditional screen-space ambient occlusion techniques by including information about geometry at multiple depth layers, using a stochastic transparency based approach. This allows us to efficiently gather the missing information in order to improve upon the accuracy and spatial stability of conventional screen-space approximations, while still maintaining the real-time performance. Our approach integrates well into existing rendering pipelines and we show how it can be generalized to work with different screen-space ambient occlusion techniques and extensions (such as bent normals and cones). We also demonstrate how multi-view stochastic-depth ambient occlusion can greatly improve upon the robustness of traditional multi-view ambient occlusion techniques. Furthermore, we provide an extensive analysis of the visual quality, robustness and performance of stochastic-depth ambient occlusion compared to conventional screen-space approaches.