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

这个项目旨在设计算法，从图像集合中推断物体的几何(形状)和光度(材料)的3-D模型。这些算法基于将视觉场景表示为密集表面，隐含地定义为测量图像的函数，并且需要数值求解偏微分方程组。我们的研究目的是在一个统一的分析框架内集成许多从图像中重建场景空间属性的“来自X的形状”算法，包括立体感、从阴影恢复形状和从运动恢复形状。我们计划开发的技术的应用范围从地质到医学、制造、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.