CAREER: Reconstructing 3D Models from Today?s Scanning Devices

职业：利用当今的扫描设备重建 3D 模型

• 批准号: 0746039
• 负责人: Michael Kazhdan
• 金额: $ 40万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-15 至 2014-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0746039&HistoricalAwards=false
• 关键词: CAREER; Reconstructing; 3D; Models; Today

AbstractPI ? Kazhdan, MichaelAdvances in 3D scanning technology have provided an essential means for acquiring information about surfaces and shapes in the real world and have played an important role in fields ranging from the humanities (e.g. cultural preservation) to the sciences (e.g. medical imaging). This research contributes to this trend by developing algorithms for reconstructing 3D models from the raw data returned by today's acquisition modalities. The challenges focused on in this work are two-fold: First, to be useful in practice, the algorithms must be able to process huge datasets, often too large to be able to fit into the working memory of an application. Second, the algorithms must be designed to be versatile, capable of adapting to the varying and non-uniform data returned by the different scanners.To address these challenges, the investigators reduce the problem of surface reconstruction to the solution of a Poisson equation which can be solved over an octree adapted to the scanned data. Specifically, this research makes three separate contributions. First, it describes a novel algorithm for solving the global Poisson system in a local manner, providing a solver that only requires a small subset of the system to be maintained in working memory at any given time. Second, it presents a novel data-structure that enables streaming traversal through an octree, making it possible to process high-resolution data that is too large to fit into memory. And third, using a finite elements formulation, it generalizes a method for model fitting that allows functions to be fit to data sampled using non-point primitives, extending the breadth of acquisition modalities supported by the reconstruction algorithm.

抽象PI？ Kazhdan，Michael 3D 扫描技术的进步为获取现实世界中的表面和形状信息提供了重要手段，并在人文学科（例如文化保护）到科学（例如医学成像）等领域发挥了重要作用。这项研究通过开发根据当今采集方式返回的原始数据重建 3D 模型的算法，为这一趋势做出了贡献。这项工作面临的挑战有两个：首先，为了在实践中有用，算法必须能够处理巨大的数据集，这些数据集通常太大而无法放入应用程序的工作内存中。其次，算法必须设计为通用的，能够适应不同扫描仪返回的变化和不均匀的数据。为了解决这些挑战，研究人员将表面重建问题简化为泊松方程的解，该方程可以通过适应扫描数据的八叉树来求解。具体来说，这项研究做出了三项不同的贡献。首先，它描述了一种以局部方式求解全局泊松系统的新颖算法，提供了一种只需要在任何给定时间在工作内存中维护系统的一小部分子集的求解器。其次，它提出了一种新颖的数据结构，可以通过八叉树进行流式遍历，从而可以处理太大而无法放入内存的高分辨率数据。第三，使用有限元公式，它概括了一种模型拟合方法，允许函数拟合使用非点基元采样的数据，扩展了重建算法支持的采集模式的广度。