CAREER: Scale Variability of 3D Geometry for Computer Vision

职业：计算机视觉 3D 几何的尺度变化

• 批准号: 0746717
• 负责人: Ko Nishino
• 金额: $ 45万
• 依托单位: Drexel University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-04-01 至 2014-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0746717&HistoricalAwards=false
• 关键词: CAREER; Scale; Variability; 3D; Geometry

Title: CAREER: Scale Variability of 3D Geometry for Computer VisionPI: Ko NishinoInstitution: Drexel UniversityWith the advent of active and passive 3D sensing techniques, 3D geometric data now play vital roles in many applications spanning a broad range of disciplines. Yet, little attention has been given to the fact that real-world objects and scenes consist of geometric structures of varying scales. For instance, a human face has a handful of discriminative local surface structures that span a wide range of spatial extents, such as the forehead, chin, nose, eyes, mouth, nostrils, earlobes, wrinkles, and dimples, from large to small scales. The relative sizes and the spatial configuration of these local structures collectively define the characteristic geometry of the face. In turn, if extracted properly, they add significant information for accurately describing the geometry of the object or scene. The goal of this research program is to establish a general theoretical and computational foundation for analyzing and exploiting this hidden dimension of 3D geometry -- the geometric scale variability. At the heart of the research program are the investigation and derivation of a formal scale-space representation of surface geometry, novel local and global geometric representations that faithfully encode the scale variability, and novel computational methods for leveraging the extra scale-related information in a number of important applications. These key componential research thrusts will individually and collectively enable one to unveil and harness the hidden characteristic properties of 3D geometry.This research program also focuses on investigating the use of geometric scale variability in a number of fundamental applications, including 3D matching, registration, and recognition, all of which serve as vital building blocks in many other applications that use 3D geometric data. The research will lead to not only more robust and efficient analysis and processing of 3D geometry, but will also enable novel approaches to handling geometry, for instance stitching together range images just like mosaicing intensity images, and set the foundation for novel use of 3D geometric data, for example, in appearance modeling. Due to the ubiquitous use of geometric data, the results are expected to have a significant impact across a broad range of disciplines, especially in nationally and societally vital domains. For instance, it will enable finer analysis of anomalous geometric structures of human organs, such as those recovered with 3D endoscopy, leading to more accurate medical diagnosis; provide rich discriminative information for sorting and matching a large collection of geometric data as often encountered in digital archaeology; and undoubtedly serve as an integral component of any 3D sensing-based surveillance application for homeland security. The use of geometric scale variability can go far beyond these examples, leading to a new paradigm for exploiting 3D geometric data.URL: http://www.cs.drexel.edu/~kon/gscale/

标题：Career：计算机视觉中3D几何的尺度可变性PI：Ko NishinoInstitution：Drexel University随着主动和被动3D传感技术的出现，3D几何数据现在在跨越广泛学科的许多应用中发挥着重要作用。然而，很少有人注意到这样一个事实，即现实世界的对象和场景由不同尺度的几何结构组成。例如，一个人的脸有几个有区别的局部表面结构，这些结构跨越了从大到小的各种空间范围，如前额、下巴、鼻子、眼睛、嘴巴、鼻孔、耳垂、皱纹和酒窝。这些局部结构的相对大小和空间配置共同定义了面部的特征几何形状。反过来，如果正确提取，它们将添加重要信息，用于准确描述对象或场景的几何体。该研究计划的目标是为分析和利用3D几何的这个隐藏的维度--几何比例可变性--建立一个普遍的理论和计算基础。研究计划的核心是研究和推导曲面几何的正式尺度空间表示，忠实地编码尺度可变性的新的局部和全局几何表示，以及在许多重要应用中利用额外的尺度相关信息的新的计算方法。这些关键组成部分的研究将单独和共同地使人们能够揭示和利用3D几何的隐藏特征属性。本研究计划还专注于研究几何比例可变性在一些基本应用中的使用，包括3D匹配、配准和识别，所有这些都是许多其他使用3D几何数据的应用程序的重要构建块。这项研究不仅将导致对3D几何的更健壮和高效的分析和处理，而且还将使处理几何的新方法成为可能，例如像拼接强度图像一样将距离图像拼接在一起，并为3D几何数据的新应用奠定基础，例如在外观建模中。由于几何数据的普遍使用，预计结果将对广泛的学科产生重大影响，特别是在国家和社会重要领域。例如，它将能够对人体器官的异常几何结构进行更精细的分析，例如通过3D内窥镜恢复的器官，从而导致更准确的医疗诊断；为数字考古中经常遇到的大量几何数据的分类和匹配提供丰富的区别性信息；毫无疑问，它将成为任何基于3D传感的国土安全监控应用程序的不可或缺的组成部分。几何比例可变性的使用远远超出了这些示例，从而产生了一种利用3D几何数据的新范式。URL：http://www.cs.drexel.edu/~kon/gscale/