Steerable Appearance Modeling of Three-Dimensional Scenes

三维场景的可操纵外观建模

• 批准号: 9530985
• 负责人: Charles Dyer
• 金额: $ 27.59万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-06-01 至 2000-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9530985&HistoricalAwards=false
• 关键词: Steerable; Appearance; Modeling; Three; Dimensional

The goal of this research is to develop appearance-based representations of three-dimensional scenes that permit images corresponding to a continuous range of views of the scene to be synthesized by adaptively combining a set of basis images. These steerable appearance models analytically model the effects of a moving camera on scene appearance. The approach is based on decomposing scene appearance into three separate components: epipolar motion, visibility, and photometric properties, which together determine how the scene will appear from arbitrary viewpoints. Each component is represented and steered independently before being combined to produce views of the scene. The components are directly computable from the basis images under certain conditions, avoiding traditional difficulties caused by ill-posed problems and complex 3-D scene recovery tasks. The research centers around the dual problems of measurability and steerability, which provide a formal basis for designing and evaluating the proposed models. Measurability defines the conditions under which it is possible to recover a representation from a set of basis images. Steerability specifies the range of views that may be reconstructed as well as the mechanism for producing them. Results should provide a better theoretical understanding of the dynamic effects of viewpoint on scene appearance. Potential applications of steerable appearance models will be investigated, such as three dimensional object recognition based on viewpoint-dependent grayscale appearance, and visual map construction of unknown three-dimensional environments by active exploration.

的 该研究的目标是开发三维场景的基于外观的表示，其允许通过自适应地组合一组基本图像来合成与场景的连续视图范围相对应的图像。 这些可操纵的外观模型分析模型的影响，一个移动的相机上的场景外观。 该方法基于将场景外观分解为三个独立的组件：极线运动，可见性和光度特性，它们共同确定场景将如何从任意视点出现。 每个 组分是 表示 和 在被组合以产生场景的视图之前独立地操纵。 这些分量可以直接从基础图像计算出来 下 某些 条件， 避免 由不适定问题和复杂的3D场景恢复任务引起的传统困难。 研究围绕可测性和可操纵性的双重问题，这为设计和评估所提出的模型提供了形式化的基础。 可测量性定义了在何种条件下可以恢复表示 从一组基本图像中。 可操纵性指定了可以重建的视图范围以及生成它们的机制。 结果应该提供一个更好的理论理解的动态效果的观点 在现场出现。 潜在应用 可操纵的外观模型将被调查，如三维 基于视点相关灰度外观的目标识别，以及通过主动探索未知三维环境的视觉地图构建。