NSF Young Investigator: Studies in Image Synthesis

真实感图像合成是建筑、照明和工业设计、卫星和机器人遥感、科学可视化、仿真系统、娱乐和广告等不同领域的一个重要问题。它还需要大量的计算。因此，仔细选择近似值对于提高计算效率至关重要。不幸的是，很少有这样的近似允许良好的误差度量，这使得控制图像保真度变得困难。在许多应用中，这种准确性是至关重要的。本研究为一般光传输系统开发了一种控制精度的渲染方法，既提供了可靠的精度界限，又允许算法集中精力减少最大的误差。除了真实感图像合成之外，研究还开始于非真实感渲染，这是一个具有巨大商业潜力的领域，迄今为止受到的关注相对较少。机制的发展，以支持生成有效的插图从三维连续色调图像。具体的研究目标包括创建传统插图技术的分类；开发新的建模范例、成像模型和用于指定和计算插图的用户交互方法；建立从具有更高细节层次的模型简化几何模型的方法；并开发模拟传统艺术家工具的技术，如铅笔、粉笔、空气刷和水彩。

Photorealistic image synthesis is an important problem for such diverse fields as architecture, lighting and industrial design, remote sensing from satellites and robots, scientific visualization, simulation systems, and entertainment and advertising. It also requires enormous computation. Carefully chosen approximations are therefore essential for making the computation efficient. Unfortunately, few such approximations admit good error metrics, which makes controlling image fidelity difficult at best. In many applications this accuracy is vital. This research develops a controlled-precision rendering approach for general systems of light transport both to provide reliable bounds on accuracy, and also to allow an algorithm to concentrate its effort where the error reduction is largest. In addition to photorealistic image synthesis, research begins in non-photorealistic rendering, an area with great commercial potential that has received relatively little attention to date. Mechanisms are developed to support the generation of effective illustrations from three-dimensional continuous-tone images. Specific research goals include creating a taxonomy of traditional illustration techniques; developing new modeling paradigms, imaging models, and user-interaction methods for specifying and computing illustrations; establishing methods for simplifying geometric models from models with a higher level of detail; and developing techniques for simulating traditional artist tools, such as pencil, chalk, air brush, and watercolor.