三维模型表面超分辨率建模数据
收藏资源简介：
通过采集高分辨率三维模型数据，利用深度生成模型进行训练，算法可以根据低分辨率模型生成高分辨率的三维模型。这一数据适用于计算机图形学、3D打印、虚拟现实和增强现实等领域，用户能够通过生成的高分辨率三维表面模型实现更逼真的三维显示和交互，解决传统低分辨率三维建模不清晰的问题。使用三维表面超分算法，提高三维模型的分辨率和精度。具体过程如下：（1）数据收集：在现有数据集收集目标三维模型。（2）数据处理：使用SDF采样算法，将高分辨率三维模型采样成高分辨率符号距离数据。使用均匀采样算法，生成低分辨率三维模型和低分辨率符号距离数据。（3）三维生成：使用低分辨率符号距离数据生成高分辨率三维物体表面模型，公式为SDF_hro = Generator_sr(SDF_lro)，其中SDF_hro表示生成的高分辨率符号距离数据,SDF_lro表示输入的低分辨率符号距离数据，Generator_sr为超分辨率生成模型。再通过MatchingCubes算法得到高分辨率三维模型，并使用平均fid进行质量评估。
By collecting high-resolution 3D model datasets and training with deep generative models, the algorithm can generate high-resolution 3D models from low-resolution input ones. This dataset is suitable for fields such as computer graphics, 3D printing, virtual reality (VR), and augmented reality (AR). Users can leverage the generated high-resolution 3D surface models to achieve more realistic 3D display and interaction, resolving the ambiguity issue of traditional low-resolution 3D modeling outputs. 3D surface super-resolution algorithms are utilized to enhance the resolution and precision of 3D models. The specific workflow is as follows: (1) Data Collection: Collect target 3D models from existing datasets. (2) Data Preprocessing: Use the Signed Distance Function (SDF) sampling algorithm to convert high-resolution 3D models into high-resolution signed distance data. Then apply uniform sampling to generate low-resolution 3D models and corresponding low-resolution signed distance data. (3) 3D Generation: Generate high-resolution 3D object surface models using the low-resolution signed distance data, following the formula: $SDF_{hro} = Generator_{sr}(SDF_{lro})$, where $SDF_{hro}$ represents the generated high-resolution signed distance data, $SDF_{lro}$ denotes the input low-resolution signed distance data, and $Generator_{sr}$ is the super-resolution generative model. Subsequently, derive the high-resolution 3D model via the Matching Cubes algorithm, and evaluate the model quality using mean Fréchet Inception Distance (mean FID).
