东方瑰宝唐卡《阿弥陀佛（极乐世界）》高清数字化3D 模型数据集
收藏资源简介：
（一）实质性加工 数据清洗与优化：针对 OBJ 格式模型，结合唐卡 “多层场景 + 精细纹样” 特征，定向排查并删除扫描产生的冗余顶点（如宝树叶片间隙的无效数据）、场景元素重叠面（如飞天飘带与宫殿屋檐衔接处），在保留细节的前提下减少 25%-30% 数据量，提升模型加载与渲染速度，满足多场景流畅运行需求。 纹理映射调整：依据唐卡矿物颜料特性（如石青的饱和度、金箔的光泽感），精确校准纹理坐标，确保颜料纹理、金线纹样贴合模型表面，避免出现线条拉伸、色彩错位； （二）创造性劳动 模型精细化处理：使用 Geomagic 软件消除扫描产生的叠面、交叉面，对阿弥陀佛莲花座花瓣、宝树叶片等精细结构进行 “网格优化”，在保证形态精准的前提下减少三角面数量；针对飞天飘带的飘逸感，通过调整曲率参数优化模型轮廓，确保线条流畅自然。 表面与纹理优化：运用 AI 自适应高斯滤波算法处理扫描噪点，重点保留矿物颜料的晕染渐变细节（如莲花花瓣的色彩过渡），避免算法过度平滑导致的质感丢失；结合人工二次校验，排查 AI 未识别的颜料色彩偏差、纹样断层问题，参考实体唐卡色卡校准色值，确保模型纹理还原唐卡真实视觉效果。
(1) Substantive Processing Data Cleaning and Optimization: For OBJ-format 3D models, incorporating the characteristics of Thangka paintings—"multi-layered scenes + intricate patterns"—we targetedly identify and remove redundant vertices generated during scanning (e.g., invalid data in the gaps between the leaves of the sacred baoshu tree) and overlapping faces of scene elements (e.g., the junction between flying apsaras' ribbons and palace eaves). This reduces the data volume by 25%-30% while preserving all details, improving model loading and rendering speeds to meet the demand for smooth operation across multiple scenarios. Texture Mapping Adjustment: Based on the properties of mineral pigments used in Thangkas (e.g., the saturation of azurite and the luster of gold foil), we accurately calibrate texture coordinates to ensure that pigment textures and gold-thread patterns fit perfectly onto the model surface, preventing line stretching and color misalignment. (2) Creative Labor Model Refinement: Using Geomagic software, we eliminate overlapping and intersecting faces generated during scanning, and perform "mesh optimization" on fine structures such as the petals of Amitabha Buddha's lotus pedestal and the leaves of the sacred baoshu tree, reducing the number of triangular faces while ensuring accurate morphology. For the flowing shape of flying apsaras' ribbons, we optimize the model outline by adjusting curvature parameters to ensure smooth and natural lines. Surface and Texture Optimization: We use an AI adaptive Gaussian filtering algorithm to remove scanning noise, prioritizing the retention of gradient details from mineral pigment rendering (e.g., the color transition of lotus petals) to avoid texture loss caused by over-smoothing from the algorithm. Combined with manual secondary verification, we identify pigment color deviations and pattern discontinuities that the AI failed to detect, and calibrate color values by referencing physical Thangka color charts to ensure that the model's textures restore the true visual effect of the original Thangka.
