The Stanford 3D Scanning Repository

本文详细介绍了斯坦福大学的3D扫描与重建资源库,包括扫描和重建过程,使用的设备和软件,以及数据存储格式。特别强调了如何获取和查看PLY文件格式的数据。

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The Stanford 3D Scanning Repository

In recent years, the number of range scanners and surface reconstruction algorithms has been growing rapidly. Many researchers, however, do not have access to scanning facilities or dense polygonal models. The purpose of this repository is to make some range data and detailed reconstructions available to the public. Here's how the models in this repository were created:

Scanning and surface reconstruction

The first set of models below, called "The Stanford Models", were scanned with a Cyberware 3030 MS scanner, with the exception of Lucy, who was scanned with the Stanford Large Statue Scanner, designed for the Digital Michelangelo Project. Both scanners are swept-stripe, laser triangulation range scanners. The triangulation calculations all the Stanford models except the Happy Buddha and Dragon were performed in hardware by the Cyberware scanner(s). These last two models were acquired using Brian Curless's spacetime analysis. Each scan takes the form of a range image, described in the local coordinate system of the scanner. To merge these range images, we must first align them together. For all the Stanford models, alignment was done using a modified ICP algorithm, as described in this paper. These alignments are stored in ".conf" files, which list each range image in the model along with a translation and a quaternion rotation. Finally, the aligned range images are combined to produce a single triangle mesh (a process sometimes called surface reconstruction) using either zippering or volumetric merging, two methods developed at Stanford. The entry for each model indicates which method was used. Implementations of both methods are currently available for download, respectively, at ZipPack and VripPack. The second method is the surface reconstruction method invoked by the Scanalyze software package used in the Digital Michelangelo Project. Another software package that might be of interest is Volfill, our diffusion-based hole filler for large polygon meshes.

The second set of models below were acquired at a XY scan resolution of 100 microns using the XYZ RGB auto-synchronized camera, which is based on technology developed in the Visual Information Technology group of the Canadian National Research Council (NRC). This camera has an accuracy (3 Sigma) of ± 0.025mm (±0.001"), and X, Y, and Z-axis resolutions of 0.1mm (0.004"), 0.002mm (0.00008"), and 0.003mm (0.0001"), respectively, as determined using a DEA Scirocco coordinate measuring machine. All post-processing, including alignment, merging, editing, and polygon reduction, were done using Innovmetric's Polyworks software. These models come to us courtesy of Helmut Kungl.

File format

Unless otherwise noted, the range data and reconstructed models in this repository are stored in PLY files. This format was developed at Stanford University, and the source code is available for download. For convenience, we have represented most of these PLY files in their ASCII formats. Choosing ASCII makes it possible for someone unfamiliar with it to get a feel for the file format, and it avoids the problem of using the correct big-endian vs. little-endian byte orders. To view PLY files, you can download our Scanalyze software package.

### 关于人脸三维模型数据集的下载 对于获取用于人脸三维模型构建的数据集,可以利用多个公开资源。例如,在研究领域内广泛使用的3D Morphable Model(3DMM),其开发基于大规模的人脸数据库,并能够通过线性变换将二维图像转换成三维模型[^2]。 #### KITTI 数据集 KITTI 是一个知名的计算机视觉算法评测平台,提供了多种类型的传感器数据,虽然主要用于自动驾驶场景下的物体检测、跟踪等任务,但也包含了部分可用于人脸识别和重建的任务设置[^3]。 然而,针对专门的人脸三维模型需求,更合适的选择可能是: #### The Stanford 3D Scanning Repository 此存储库由斯坦福大学维护,提供了一系列高质量的扫描对象,其中包括一些面部扫描结果。这些模型通常以PLY格式保存,可以直接用于实验或作为训练样本的一部分。访问链接如下: - [The Stanford 3D Scanning Repository](http://graphics.stanford.edu/data/3Dscanrep/) 另外还有ShapeNet数据集中也含有丰富的3D模型资源,尽管它并非专门为脸部设计,但在某些情况下也可以找到适用的脸部模型实例。ShapeNet中的点云数据可以通过特定链接获得,其中包含`.pts`格式的点云文件及其对应的标签信息[^4]: - 百度网盘链接:[ShapeNet PTS Format Point Cloud Data](https://pan.baidu.com/s/1MavAO_GHa0a6BZh4Oaogug),提取码:3hoe 为了更好地理解如何应用上述提到的技术与工具,下面给出一段简单的MATLAB代码片段展示如何加载并处理来自预训练好的3DMM模型的数据: ```matlab % 加载预先训练好的3DMM模型 load('3DMM_model.mat'); % 假设已有一个名为'3DMM_model.mat' 的预训练模型文件 % 调用函数reconstruct3DModel() 进行人脸三维重建 [shape, texture] = reconstruct3DModel(landmarks, model); ``` 这段代码展示了基本的操作流程,即先加载必要的模型参数,再调用相应的函数完成具体的三维重建工作[^1]。
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