CAREER: Nonlinear Processing of Light Transport Data for Realistic Computer Imagery

职业：光传输数据的非线性处理以实现逼真的计算机图像

• 批准号: 0746577
• 负责人: Rui Wang
• 金额: $ 40万
• 依托单位: University of Massachusetts Amherst
• 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: 2008-08-01 至 2014-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0746577&HistoricalAwards=false
• 关键词: CAREER; Nonlinear; Processing; Light; Transport

Abstract:CAREER: Nonlinear Processing of Light Transport Data for Realistic Computer ImageryPI: Rui WangRealistic computer graphics involving very large image datasets, such as lightfields, precomputed light transport, time-lapse video, measured material database, has become an increasingly important and interesting topic in recent years. While rendering systems can exploit these data to achieve greatly improved performance and visual fidelity, such datasets also present significant challenges for efficient processing, not only because of their large scale and high dimensionality, but more importantly, because of their intrinsic nonlinear structures. That is, the data naturally contain very high frequency, rapidly changing information that cannot be adequately represented using linear processing models that prevail elsewhere in computer graphics.This project develops nonlinear processing methods for large-scale, multidimensional light transport data. These nonlinear methods adaptively select the best solutions for representation driven by the data?s intrinsic nonlinear structure, thereby significantly improving the sampling, compression, reduction, and reconstruction of such data. The project involves three steps of research. The first step collects light transport data from both simulated and measured sources, and derives a novel sampling strategy by using data-driven analysis. In the second step, the PI investigates several new methods for nonlinearly reducing and approximating light transport data into compact representations. These methods include nonlinear basis projection, clustered piecewise constant approximation, and augmented dimensionality reduction. The third step develops efficient computational methods using the compressed data representation for fast reconstruction and real-time visualization. These methods are naturally amenable to acceleration on modern graphics hardware. The resulting new approaches from this research can leverage broader applications in other fields that involve similar types of image datasets, such as geosciences, medical imaging, and forensic analysis.

摘要：Career：面向真实感计算机图像的光传输数据的非线性处理PI：Ri Wang现实计算机图形学涉及非常大的图像数据集，如光场、预计算光传输、延时视频、测量材料数据库，近年来已成为一个越来越重要和有趣的话题。虽然渲染系统可以利用这些数据来实现极大的性能和视觉保真度，但这些数据集也对高效处理提出了巨大的挑战，不仅因为它们的规模和高维，更重要的是因为它们固有的非线性结构。也就是说，数据自然包含非常高频率、快速变化的信息，这些信息不能用计算机图形学中流行的线性处理模型来充分表示。本项目开发了针对大规模、多维光传输数据的非线性处理方法。这些非线性方法自适应地选择数据驱动的最优解进行表示--S固有的非线性结构，从而显著改善了这类数据的采样、压缩、约简和重建。该项目包括三个研究步骤。第一步从模拟源和实测源收集光传输数据，并利用数据驱动分析得出一种新的采样策略。在第二步中，PI研究了几种新的方法来将光传输数据非线性地简化和逼近为紧凑的表示。这些方法包括非线性基投影法、聚类分段常数逼近法和增广降维方法。第三步利用压缩数据表示法开发高效的计算方法，以实现快速重建和实时可视化。这些方法自然适用于现代图形硬件上的加速。这项研究产生的新方法可以在涉及类似类型的图像数据集的其他领域利用更广泛的应用，例如地球科学、医学成像和法医分析。