A Variational Framework for Reconstructing 3D Shape and Photometry from Multiple Images

该项目旨在设计算法，从图像集合中推断物体的几何（形状）和光度（材料）的三维模型。这样的算法是基于一个代表的视觉场景作为密集的表面，隐含地定义为测量图像的函数，并需要偏微分方程的数值解。我们的研究旨在整合在一个统一的分析框架中的许多“形状从X”的算法重建的空间属性的场景从图像，包括立体，形状从阴影，和形状从运动。我们计划开发的技术应用范围从地质学到医学、制造业、安全到娱乐。

This project aims at designing algorithms to infer 3-D models of the geometry (shape) and photometry (material) of objects from collections of images. Such algorithms are based on a representing visual scenes as dense surfaces, defined implicitly as functions of the measured images, and entail the numerical solution of partial differential equations. Our research aims at integrating in a unified analytical framework many ``shape from X'' algorithms for reconstructing spatial properties of a scene from images, including stereo, shape from shading, and shape from motion. Applications of the technology we plan to develop ranges from geology to medicine, manufacturing, security, to entertainment.