Collaborative Research: An ADT Proposal: Fast Point Cloud Surface Reconstruction Algorithms

协作研究：ADT提案：快速点云表面重建算法

• 批准号: 0915231
• 负责人: Ronald DeVore
• 金额: $ 70.79万
• 依托单位: Texas A&M Research Foundation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0915231&HistoricalAwards=false
• 关键词: Collaborative; Research; ADT; Proposal; Fast

Sensor data is vital to the security, economy, and health of our nation. It comes in various formats and resolutions: visual, radar, lidar,laser scans, and multiple forms of medical imagery. There have emerged several approaches to the representation and visualization of point cloud data. These developments have typically been ad hoc, applying to one form of sensor data. This project will develop new generic processing platforms based on the following key ingredients. The quality of fit will typically not be made in the least squares sense but instead will be determined by metrics closely tied to the application domain such as variants of the Hausdorff metric. The representation platform will be a fusion of implicit (level set) methods together with multiscale (wavelet like) decompositions. The selection of optimal representations will be made with the aid of techniques from nonlinear approximation and learning theory. The proposed research will be applicable to generic point cloud data, but an emphasis will be placed on processing point cloud data that arise in applications such as autonomous navigation of robots and micro air vehicles, identification of sources of biological and chemical contaminants and climate modeling. Information about the world in which we live is obtained through sensors. These include medical imagery (MRIs, CAT scans), navigational tools (Radar, LIDAR, Sonar), surveillance (satellite imagery, video). The data gathered by such sensors consist of an array of point values (called a point cloud). This data must be processed and visualized in order to extract the important information they hold. Such processing is done literally millions of times a day and how it is done determines the quality of the information. Mostcurrent methods for processing and visualizing sensor data are built on old ideas from image processing and fail to capture many of their important features such as their geometry and topology. This project proposes new sophisticated techniques from mathematics and computer science to create more effective data processing. The development will be made with an eye towards the critical issues of accuracy and the speed at which the processing and rendering take place. A particular emphasis in the proposal is on terrain datawhich are used daily in navigation, especially for robotics and unmanned air surveillance.

传感器数据对我们国家的安全，经济和健康至关重要。 它采用各种格式和分辨率：视觉，雷达，激光扫描和多种形式的医学图像。 有几种方法来表示点云数据的表示和可视化。 这些发展通常是临时的，适用于一种形式的传感器数据。 该项目将根据以下关键成分开发新的通用处理平台。 拟合的质量通常不会在最小二乘意义上没有达到，而是由与应用程序域紧密相关的指标（例如Hausdorff Metric的变体）确定的。 表示平台将是隐式（级别集）方法的融合，以及多尺度（小波类似）分解。 最佳表示的选择将借助非线性近似和学习理论的技术进行。 拟议的研究将适用于通用点云数据，但是将重点放在处理点云数据中，这些数据在诸如机器人和微型航空车辆的自动导航中出现的处理点云数据，生物和化学污染物以及气候建模的来源的鉴定。 有关我们生活的世界的信息是通过传感器获得的。 其中包括医学图像（MRI，CAT扫描），导航工具（雷达，激光雷达，声纳），监视（卫星图像，视频）。 该传感器收集的数据由一个点值组成（称为点云）。 必须对此数据进行处理和可视化，以提取其持有的重要信息。 这种处理实际上是每天数百万次进行的，以及如何完成该处理可以决定信息的质量。大多数用于处理和可视化传感器数据的方法是基于图像处理中的旧思想而建立的，并且无法捕获其许多重要特征，例如它们的几何形状和拓扑。 该项目提出了来自数学和计算机科学的新技术，以创建更有效的数据处理。 该开发将着眼于准确性的关键问题以及处理和渲染的速度。该提案中特别强调的是Datawhich的地形每天在导航中使用，尤其是用于机器人和无人机监视。