CAREER: A Statistical Framework For Reconstructing 3D Manifolds From Range Data

职业生涯：从范围数据重建 3D 流形的统计框架

• 批准号: 0092065
• 负责人: Ross Whitaker
• 金额: $ 22.53万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-10-01 至 2005-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0092065&HistoricalAwards=false
• 关键词: CAREER; Statistical; Framework; Reconstructing; 3D

Title --- CAREER: A Statistical Approach to Estimating 3D Manifolds From Range DataPI --- Ross T. WhitakerInstitution --- University of UtahThis project addresses the question of how to automatically generate 3D computer models of objects and scenes using data from a range finding device, such as a laser range scanner, sonar, ultrasound, or radar. Such 3D computer models are important in a wide range of applications including defense surveillance, forensics, teaching, and medicine. Range measuring devices typically sweep a beam of energy to gather many millions of 3D measurements from surfaces of objects but they have some limitations. First, because not all object are visible from a single point of view, a single sweep is incomplete. Second, each individual range measurement is not necessarily accurate because the measurement process is inherently noisy. The strategy is to systematically fuse together many measurements from different points of view in order to create accurate, complete 3D models. This project examines some of the fundamental mathematical questions pertaining to this process and then studies how to implement and demonstrate this theory on real data.Range-finding devices measure distances to objects by reflecting energy off of the interfaces between different types of materials, but they provide a noisy, mathematically complex, and highly nonlinear transformation from a collection of surfaces to a set 2D depth maps. This project will develop statistical methods for estimating manifolds from this kind of data, thereby generalizing the current state of the art in estimation theory, which is primarily concerned with estimating functions or fields. Thus, the goal is to provide a general, complete, and practical foundation for 3D surface reconstruction. The strategy is to find the surface that maximizes the posterior probability conditional on a collection of range measurements taken from different points of view. The reconstruction framework is Bayesian; it includes a sensor model as well as prior knowledge about the characteristics of the object or scenes being modeled. This work will address a number of important issues pertaining to this statistical methodology for building 3D models, including better sensor models, high-order priors, fast and robust algorithms, and broader applications. These developments will comprise a fundamental scientific result: the generalization of the basic principles of estimation theory to the challenging and timely problem of 3D surface reconstruction.

标题 --- 职业：根据距离数据估计 3D 流形的统计方法PI --- Ross T. WhitakerInstitution --- 犹他大学 该项目解决了如何使用来自测距设备（例如激光测距扫描仪、声纳、超声波或雷达）的数据自动生成物体和场景的 3D 计算机模型的问题。 这种 3D 计算机模型在国防监视、法医学、教学和医学等广泛应用中都很重要。 测距设备通常会扫描能量束，从物体表面收集数百万个 3D 测量结果，但它们有一些局限性。 首先，因为并非所有对象从单一角度都是可见的，所以单次扫描是不完整的。 其次，每个单独的距离测量不一定准确，因为测量过程本质上是有噪声的。 该策略是将来自不同角度的许多测量系统地融合在一起，以创建准确、完整的 3D 模型。 该项目研究了与此过程相关的一些基本数学问题，然后研究如何在实际数据上实现和演示该理论。测距设备通过从不同类型材料之间的界面反射能量来测量到物体的距离，但它们提供了从表面集合到一组二维深度图的噪声、数学复杂和高度非线性的转换。 该项目将开发用于从此类数据估计流形的统计方法，从而概括估计理论的当前技术水平，该理论主要涉及估计函数或域。 因此，我们的目标是为 3D 表面重建提供通用、完整且实用的基础。 该策略是找到使后验概率最大化的表面，条件是从不同角度进行的距离测量的集合。 重构框架为贝叶斯；它包括传感器模型以及有关正在建模的对象或场景的特征的先验知识。 这项工作将解决与构建 3D 模型的统计方法相关的许多重要问题，包括更好的传感器模型、高阶先验、快速且稳健的算法以及更广泛的应用。 这些进展将构成一个基本的科学成果：将估计理论的基本原理推广到具有挑战性且及时的 3D 表面重建问题。