Steerable Appearance Modeling of Three-Dimensional Scenes

本研究的目标是开发基于外观的三维场景表示，允许通过自适应组合一组基本图像来合成对应于场景连续视图范围的图像。这些可操纵的外观模型解析地模拟了移动摄像机对场景外观的影响。该方法基于将场景外观分解为三个独立的组件：极外运动，可见性和光度属性，它们共同决定了场景从任意视点呈现的方式。在组合生成场景视图之前，每个组件都是独立表示和引导的。在一定条件下，构件可直接从基图中计算，避免了传统的不适定问题和复杂的三维场景恢复任务所带来的困难。研究围绕着可测量性和可操纵性的双重问题，为模型的设计和评价提供了形式化的基础。可测量性定义了从一组基本图像中恢复表示的可能条件。可操作性指定了可重构视图的范围以及生成视图的机制。研究结果将为视点对场景外观的动态影响提供更好的理论理解。将研究可操纵外观模型的潜在应用，例如基于视点依赖的灰度外观的三维物体识别，以及通过主动探索构建未知三维环境的视觉地图。

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.