Recently, it becomes popular to synthesize new viewpoint images based on some sampled viewpoint images of real scene using technique of computer vision. 3D shape reconstruction in Euclidean space is not necessarily required, but information of dense matching points is basically enough to synthesize new viewpoint images. In this paper, we propose a new method for 3D reconstruction from three cameras based on projective geometry. In the proposed method, three input camera images are rectified based on projective geometry, so that the vertical and horizontal directions can be completely aligned with the epipolar planes between the cameras. This rectification provides Projective Voxel Space (PVS), in which the three axes are aligned with the directions of camera projection. Such alignment simplifies the procedure for projection between the 3D space and the image planes in PVS. Taking this advantage of PVS, silhouettes of the objects are projected into PVS, so that the searching area of matching points can be reduced. The consistency of color value between the images is also evaluated for final determination of the matching point. The finally acquired matching points in the proposed method are described as the surface of the objects in PVS. The acquired surface of the objects in PVS also includes knowledge about occlusion. Finally, images from new viewpoints can be synthesized from the matching points and occlusions. Although the proposed method requires only weak calibration, plausible occlusions are also synthesized in the images. In the experiments, images of virtual viewpoints, which were set among three cameras, are synthesized from three real images.
|ジャーナル||IEICE Transactions on Information and Systems|
|出版ステータス||Published - 2001 12月|
ASJC Scopus subject areas
- コンピュータ ビジョンおよびパターン認識