Data-Driven Modeling of Shape, Reflection, and Interreflection

形状、反射和互反射的数据驱动建模

• 批准号: 0413198
• 负责人: Steven Seitz
• 金额: $ 28万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-11-15 至 2008-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0413198&HistoricalAwards=false
• 关键词: Data; Driven; Modeling; Shape; Reflection

The goal of this project is to develop robust algorithms for reconstructing the 3D shape and reflectance properties of real world scenes. The distinguishing feature of the proposed approach is the modeling of global light transport, taking into account both realistic reflection and global interreflection of light with surfaces in the unknown scene. Modeling realistic light transport is a open problem with major importance in computer vision, due to the fact that everyday materials reflect light in complex ways (e.g., wood, hair, velvet), and because the appearance of geometrically complex scenes is strongly affected by interreflection. This project seeks to model reflection in a very general setting, where the shape and the reflectance properties of the scene are both unknown and unconstrained. Modeling of interreflection will focus on diffuse scattering, and fully account for global light propagation through the scene. Toward this objective, a new computational framework is introduced that uses data-driven models of light transport that can be captured directly from photographs, and recovers scene structure without the need for complex simulations or optimizations of the underlying physical process. A key advantage of such data-driven models is that they work robustly in very general conditions.The proposed work opens up new avenues in 3D shape sensing technology, a problem with widespread applications in robotics, visualization, mapping, aerial imaging, and virtual reality. The ability to capture material models on real objects will improve realism in computer graphics, and impacts applications such as entertainment, visualization, and the communication of visual media. The project will involve undergraduate and graduate research projects and the outcome will be a set of results and tools that will be broadly disseminated and incorporated into research projects and educational initiatives at the University of Washington.

这个项目的目标是开发强大的算法，用于重建真实的世界场景的三维形状和反射特性。所提出的方法的显着特点是全局光传输的建模，同时考虑到现实的反射和全局光与未知场景中的表面的相互反射。 建模真实的光传输是计算机视觉中具有重大意义的开放问题，这是由于日常材料以复杂的方式反射光的事实（例如，木材、头发、天鹅绒），并且因为几何复杂场景的外观受到相互反射的强烈影响。这个项目试图在一个非常一般的设置，其中的形状和场景的反射属性都是未知的和不受约束的反射模型。 相互反射的建模将集中在漫散射上，并充分考虑通过场景的全局光传播。 为了实现这一目标，引入了一种新的计算框架，该框架使用可以直接从照片中捕获的光传输的数据驱动模型，并恢复场景结构，而不需要对底层物理过程进行复杂的模拟或优化。这种数据驱动的模型的一个关键优势是，他们在非常一般的条件下工作鲁棒性。拟议的工作开辟了新的途径，在3D形状传感技术，一个问题，广泛应用于机器人，可视化，地图，航空成像和虚拟现实。在真实的物体上捕捉材料模型的能力将提高计算机图形学中的真实感，并影响诸如娱乐、可视化和视觉媒体通信的应用。 该项目将涉及本科生和研究生的研究项目，其成果将是一套成果和工具，将广泛传播并纳入华盛顿大学的研究项目和教育举措。