CIF: Small: Geometric, Variational Algorithms for Radiometric-Based Shape Reconstruction

CIF：小：基于辐射的形状重建的几何变分算法

• 批准号: 1526848
• 负责人: Anthony Yezzi
• 金额: $ 50万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1526848&HistoricalAwards=false
• 关键词: CIF; Small; Geometric; Variational; Algorithms

The investigator will develop a new class of variational geometric inverse algorithms for reconstructing dense 3D shape of objects from measurements of a scene representing an arbitrary combination of vantage points and/or resolution by employing a common mathematical framework based on generative radiometric models. Reconstruction of shape from raw sensor data is a necessary step for graphical 3D rendering and analysis in scenarios where CAD models are unavailable or impossible. Two examples to be initially explored include unfocussed camera images from different viewpoints and/or focal lengths using thin-lens modelling, and radar signals reflected from nearby objects with different antenna locations and/or wavelengths.The framework will be general enough to apply to several related sensor modalities beyond those initially investigated, ranging from infrared, acoustics, and SAR. Furthermore, having a unified model affords the freedom to generate flexible data capture and fusion schemes where not one, but multiple sets of measurements are captured under different viewpoint and sensor setting characteristics, and use the entire set of collected data to infer an estimate of reflectance and geometry that is of superior quality relative to what may be obtained in scenarios where "isolating a single cue" is not possible. For instance, with cameras, it may be impossible to fix the viewpoint while capturing images of different focus (isolating focus), or to capture perfectly sharp images because of the finite aperture of the lens (isolating viewpoint). Removing this constraint can enable applications to endoscopy, inspection of pipes and crevices, dental impressions, as well as environmental monitoring.

研究人员将开发一类新的变分几何逆算法，用于从代表Vantage位置和/或分辨率的任意组合的场景的测量中重建密集的3D形状的对象，通过采用基于生成辐射模型的通用数学框架。从原始传感器数据重建形状是在CAD模型不可用或不可能的情况下进行图形3D渲染和分析的必要步骤。初步探讨的两个例子包括未聚焦的相机图像从不同的角度和/或焦距使用薄透镜建模，雷达信号从附近的物体反射不同的天线位置和/或wavelength.The框架将是一般的，足以适用于几个相关的传感器模式超出了最初的调查，从红外，声学，和SAR。此外，具有统一的模型提供了生成灵活的数据捕获和融合方案的自由，其中在不同的视点和传感器设置特性下捕获的不是一个而是多个测量集合，并且使用整个收集的数据集合来推断反射率和几何形状的估计，该估计相对于在“隔离单个线索”不可能的场景中可能获得的估计具有上级质量。例如，对于照相机，可能不可能在捕获不同焦点的图像时固定视点（隔离焦点），或者由于透镜的有限孔径而捕获完全清晰的图像（隔离视点）。消除这一限制可以使应用内窥镜检查，检查管道和裂缝，牙科印模，以及环境监测。