Estimation of 3D Surface Shape and Smooth Radiance from 2D Images: A Level Set Approach |
| |
Authors: | Hailin Jin Anthony J. Yezzi Yen-Hsi Tsai Li-Tien Cheng Stefano Soatto |
| |
Affiliation: | (1) Department of Electrical Engineering, Washington University, Saint Louis, Missouri, 63130;(2) School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, 30332;(3) PACM and Mathematics Department, Princeton University, Princeton, New Jersey, 08544;(4) Department of Mathematics, University of California San Diego, La Jolla, California, 92093;(5) Computer Science Department, University of California, Los Angeles, California, 90095 |
| |
Abstract: | We cast the problem of shape reconstruction of a scene as the global region segmentation of a collection of calibrated images. We assume that the scene is composed of a number of smooth surfaces and a background, both of which support smooth Lambertian radiance functions. We formulate the problem in a variational framework, where the solution (both the shape and radiance of the scene) is a minimizer of a global cost functional which combines a geometric prior on shape, a smoothness prior on radiance and a data fitness score. We estimate the shape and radiance via an alternating minimization: The radiance is computed as the solutions of partial differential equations defined on the surface and the background. The shape is estimated using a gradient descent flow, which is implemented using the level set method. Our algorithm works for scenes with smooth radiances as well as fine homogeneous textures, which are known challenges to traditional stereo algorithms based on local correspondence. |
| |
Keywords: | variational methods Mumford– Shah functional image segmentation multi-frame stereo reconstruction partial differential equations level set methods |
本文献已被 SpringerLink 等数据库收录! |
|