## 3 Results and Discussion  
Prior to conducting comparative experiments to validate the performance of the proposed TPOT-based fur simulation algorithm, we performed a comprehensive review of mainstream techniques in 3D animal fur rendering. Specifically, we analyzed three repre  
Figure 2: Flow chart of the TPOT-based fur simulation algorithm.  
sentative approaches: (1) traditional texture mapping-based 3D modeling [16], (2) particle-based dynamic fur simulation [17], and (3) curve function-based hair morphology modeling [18]. Each method was evaluated under identical conditions using Blender and Unity AR platforms [19], focusing on rendering time, computational efficiency, and resource consumption during the generation of complex fur models. The experimental results, summarized in Table 1, provided a quantitative foundation for assessing algorithmic performance and guided the selection of two benchmark methods for direct comparison with the TPOT algorithm.  
Based on the experimental data, the traditional texture mapping algorithm exhibited the highest rendering efficiency, offering significantly faster processing speeds compared to the other two approaches. Its low computational demands make it particularly suitable for AR applications on mobile platforms. However, this method demonstrates the lowest performance in replicating realistic fur textures. In contrast, although particle-based and curve function-based hair modeling methods show comparable rendering times, the particle system provides superior visual detail and is relatively easier to implement, positioning it as a strong alternative. Consequently, traditional texture mapping and particle-based hair simulation were selected as control methods for constructing the white-lipped deer 3D model and its AR interactive display.  
To ensure biological accuracy, a detailed physical examination of a white-lipped deer specimen was conducted at the Qinghai-Tibet Nature and Wildlife Museum. Observations revealed that the deer's fur comprises long, coarse hairs with hollow medulliae, offering thermal insulation and resistance to harsh alpine conditions. Male specimens also exhibit a prominent \"creased collar\" of stiff, reversed hairs along the shoulders and upper back. Based on these morphological features, a comprehensive set of physical parameters (P) was extracted. Leveraging these parameters, the TPOT algorithm, in conjunction with ShaderMap [20], was employed to generate multiple physically based texture layers, including metallicness, ambient occlusion, and hair height maps. These layers simulate microfaceted surfaces that realistically reproduce the fur's interaction with light. Weighted blending of each texture layer P\({}_{1}\) with the base texture T\({}_{0}\) was applied to achieve a seamless composite texture. Final refinements involved the addition of microscopic hair details to further enhance visual fidelity. The overall workflow is depicted in Figure 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Project Name** & **Traditional Texture** & **Particle Hair** & **Curved Hair** \\  & **Mapping Algorithm** & **Algorithm** & **Algorithm** \\ \hline Cubic & & & \\ Number of hairs & 0 & 30,000 & 30,000 \\ Rendering engine & Cycles & Cycles & Cycles \\ Resolution & 1920 \(\times\) 1080 & 1920 \(\times\) 1080 & 1920 \(\times\) 1080 \\ Blender rendering time & 3 s & 24 s & 23 s \\ Unity AR average & & & \\ frame rate & 200 FPS & 72 FPS & 75 FPS \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative analysis of rendering efficiency and resource utilization across three mainstream 3D fur simulation algorithms, providing a quantitative basis for subsequent experimental validation of the TPOT method.