Neutrosophic Models for Stereo Depth, Scene Layers, and Confidence Estimation in 3D Animation Production Effect

This paper presents three new mathematical models based on neutrosophic logic and topological structures to improve stereo effects in 3D animation. The first model uses NeutroTopology to represent depth with truth, indeterminacy, and falsehood values, instead of single numbers. This helps handle difficult areas like occlusion or semi transparent objects. The second model applies SuperHyperTopology to divide a 3D scene into layers (such as foreground, background, etc.), treating each layer as a separate space with its own properties. This makes complex animations more accurate and easier to manage. The third model introduces Neutrosophic Probability Fusion to calculate how much we can trust depth data. It combines information from different sources (like stereo matching and motion) using a special rule that includes uncertainty. Each model is explained with equations and full examples. The results show better depth maps and more realistic 3D scenes, especially in areas that are normally hard to process.