Complex Reflectance, Texture and Shape: Methods and Representations for Object Modeling

提案#：308185标题：复杂反射，纹理和形状：对象建模的方法和表示PI：Belhumeur，PeterColumbia University这项研究是为了研究具有复杂反射比的对象的建模技术。我们的目标是开发恢复自然对象的形状和反射属性的方法--由于其固有的复杂性，这些对象被证明是传统的形状重建技术所难以捉摸的，例如双目立体、运动结构、光度立体，甚至激光测距仪。了解形状和反射率之间的相互作用对对象建模至关重要，在许多方面也是计算机视觉领域的核心。然而，这一前景看好的研究途径在过去被忽视了，要么是由于获取和存储的限制，要么是因为缺乏适当的技术。最近，PI引入了两种新的形状重建和反射恢复方法并制作了原型，这两种方法基本上利用了两个被忽视的物理原理。这两种方法，即Helmholtz立体视觉和光场重建，无论物体的反射比(BRDF)如何，都能够恢复表面形状，并且与传统的重建技术相比具有许多优点。提出的研究包括新的形状重建技术，构建对象模型的方法，包括它们的形状和逐点的BRDF，以及这些模型在基于图像的绘制中的应用。此外，这些重建技术将作为广泛研究BRDF的工具，以及外观随视点和照明而变化的3-D纹理。与目前的形状估计技术不同，所提出的方法将生成包含几何数据和BRDF数据的对象模型。对象建模技术中的这一新功能将在工业设计、建筑、娱乐、机器人和医学等领域受益。对重建对象、表面法线和形状的反射函数的估计直接导致用于计算机图形学应用的基于图像的建模和绘制的新方法。这些建模技术将用于研究三维纹理和自然材料(包括人类皮肤)的反射特性，这将有助于产生更有效的生成外观模型，可用于安全应用(例如，人脸识别)、人机交互应用、娱乐、通过视觉模拟学习、通信等。除了应用所产生的研究成果并通过同行评议的出版物传播外，资金将用于支持两名博士生在三年内的培训。

Proposal #: 308185Title: Complex Reflectance, Texture and Shape: Methods and Representations for Object ModelingPI: Belhumeur, PeterColumbia UniversityThis research is to investigate techniques for modeling objects with complex reflectance. Our aim is to develop methods for recovering both shape and reflectance properties of natural objects -- objects that due to their inherent complexity have proven elusive to traditional shape reconstruction techniques such as binocular stereo, structure from motion, photometric stereo, and even laser range finders. Understanding the interplay between shape and reflectance is critical to object modeling and is in many ways central to the field of computer vision. Yet this promising avenue of research has been overlooked in the past, either due to acquisition and storage limitations or, perhaps, due to a lack of appropriate techniques. Recently, the PI's have introduced and prototyped two novel methods for shape reconstruction and reflectance recovery thatessentially exploit two neglected physical principles. These two methods, named Helmholtz stereopsis and light field reconstruction, are able to recover surface shape regardless of the object's reflectance (BRDF), and they offer numerous advantages over conventional reconstruction techniques. The proposed research includes new shape reconstruction techniques, methods for constructing object models including their shape and BRDF on a point-by-point basis, and applications of these models to image-based rendering. In addition, these reconstruction techniques will serve as tools for a broad study of BRDF's in general as well as 3-D textures whose appearance varies with viewpoint and lighting. Unlike current shape estimation techniques, the proposed methods will produce object models with both geometric and BRDF data. This new capability in object modeling technology will be beneficial in areas such as industrial design, architecture, entertainment, robotics, and medicine. Estimation of the reflectance function across the reconstructed objects, surface normals, and shape, leads directly to new methods for image-based modeling and rendering for computer graphics applications. These modeling techniques will be used to study reflectance properties of 3-D textures and natural materials (including human skin), and this will help to produce more effective generative appearance models that can be used in security applications (e.g., face recognition), human computer interaction applications, entertainment, learning through visual simulation, communications, etc. In addition to the applications of the resulting research and its dissemination through peer-reviewed publications, funding will be used to support the training of two Ph.D. students over three years.