Multiperspective Stereo Matching and Volumetric Reconstruction

Stereo matching and volumetric reconstruction are the most explored 3D scene recovery techniques in computer vision. Many existing approaches assume perspective input images and use the epipolar constraint to reduce the search space and improve the accuracy. In this paper we present a novel framework that uses multi-perspective cameras for stereo matching and volumetric reconstruction. Our approach first decomposes a multi-perspective camera into piecewise primitive General Linear Cameras or GLCs [32]. A pair of GLCs in general do not satisfy the epipolar constraint. However, they still form a nearly stereo pair. We develop a new Graph-Cut-based algorithm to account for the slight vertical parallax using the GLC ray geometry. We show that the recovered pseudo disparity map conveys important depth cues analogous to perspective stereo matching. To more accurately reconstruct a 3D scene, we develop a new multi-perspective volumetric reconstruction method. We discretize the scene into vox...

• Type: Conference
• Year: 2009
• Where: ICCV
• Authors: Yuanyuan Ding, Jingyi Yu, and Peter Sturm
• Keywords: Multiperspective Imaging, Stereo, Volumetric Reconstruction