IEICE TRANSACTIONS on Information
Stereo Matching between Three Images by Iterative Refinement in PVS
Summary :
In the field of computer vision and computer graphics, Image-Based-Rendering (IBR) methods are often used to synthesize images from real scene. The image synthesis by IBR requires dense correct matching points in the images. However, IBR does not require 3D geometry reconstruction or camera calibration in Euclidean geometry. On the other hand, 3D reconstructed model can easily point out the occlusion in images. In this paper, we propose an approach to reconstruct 3D shape in a voxel space, which is named Projective Voxel Space (PVS). Since PVS is defined by projective geometry, it requires only weak calibration. PVS is determined by rectifications of the epipolar lines in three images. Three rectified images are orthogonal projected images of a scene in PVS, so processing about image projection is easy in PVS. In both PVS and Euclidean geometry, a point in an image is on a projection from a point on a surface of the object in the scene. Then the other image might have a correct matching point without occlusion, or no matching point because of occlusion. This is a kind of restriction about searching matching points or surface of the object. Taking advantage of simplicity of projection in PVS, the correlation values of points in images are computed, and the values are iteratively refined using the restriction described above. Finally, the shapes of the objects in the scene are acquired in PVS. The reconstructed shape in PVS does not have similarity to 3D shape in Euclidean geometry. However, it denotes consistent matching points in three images, and also indicates the existence of occluded points. Therefore, the reconstructed shape in PVS is sufficient for image synthesis by IBR.
- Publication
- IEICE TRANSACTIONS on Information Vol.E86-D No.1 pp.89-100
- Publication Date
- 2003/01/01
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- PAPER
- Category
- Image Processing, Image Pattern Recognition
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Makoto KIMURA, Hideo SAITO, Takeo KANADE, "Stereo Matching between Three Images by Iterative Refinement in PVS" in IEICE TRANSACTIONS on Information,
vol. E86-D, no. 1, pp. 89-100, January 2003, doi: .
Abstract: In the field of computer vision and computer graphics, Image-Based-Rendering (IBR) methods are often used to synthesize images from real scene. The image synthesis by IBR requires dense correct matching points in the images. However, IBR does not require 3D geometry reconstruction or camera calibration in Euclidean geometry. On the other hand, 3D reconstructed model can easily point out the occlusion in images. In this paper, we propose an approach to reconstruct 3D shape in a voxel space, which is named Projective Voxel Space (PVS). Since PVS is defined by projective geometry, it requires only weak calibration. PVS is determined by rectifications of the epipolar lines in three images. Three rectified images are orthogonal projected images of a scene in PVS, so processing about image projection is easy in PVS. In both PVS and Euclidean geometry, a point in an image is on a projection from a point on a surface of the object in the scene. Then the other image might have a correct matching point without occlusion, or no matching point because of occlusion. This is a kind of restriction about searching matching points or surface of the object. Taking advantage of simplicity of projection in PVS, the correlation values of points in images are computed, and the values are iteratively refined using the restriction described above. Finally, the shapes of the objects in the scene are acquired in PVS. The reconstructed shape in PVS does not have similarity to 3D shape in Euclidean geometry. However, it denotes consistent matching points in three images, and also indicates the existence of occluded points. Therefore, the reconstructed shape in PVS is sufficient for image synthesis by IBR.
URL: https://global.ieice.org/en_transactions/information/10.1587/e86-d_1_89/_p
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@ARTICLE{e86-d_1_89,
author={Makoto KIMURA, Hideo SAITO, Takeo KANADE, },
journal={IEICE TRANSACTIONS on Information},
title={Stereo Matching between Three Images by Iterative Refinement in PVS},
year={2003},
volume={E86-D},
number={1},
pages={89-100},
abstract={In the field of computer vision and computer graphics, Image-Based-Rendering (IBR) methods are often used to synthesize images from real scene. The image synthesis by IBR requires dense correct matching points in the images. However, IBR does not require 3D geometry reconstruction or camera calibration in Euclidean geometry. On the other hand, 3D reconstructed model can easily point out the occlusion in images. In this paper, we propose an approach to reconstruct 3D shape in a voxel space, which is named Projective Voxel Space (PVS). Since PVS is defined by projective geometry, it requires only weak calibration. PVS is determined by rectifications of the epipolar lines in three images. Three rectified images are orthogonal projected images of a scene in PVS, so processing about image projection is easy in PVS. In both PVS and Euclidean geometry, a point in an image is on a projection from a point on a surface of the object in the scene. Then the other image might have a correct matching point without occlusion, or no matching point because of occlusion. This is a kind of restriction about searching matching points or surface of the object. Taking advantage of simplicity of projection in PVS, the correlation values of points in images are computed, and the values are iteratively refined using the restriction described above. Finally, the shapes of the objects in the scene are acquired in PVS. The reconstructed shape in PVS does not have similarity to 3D shape in Euclidean geometry. However, it denotes consistent matching points in three images, and also indicates the existence of occluded points. Therefore, the reconstructed shape in PVS is sufficient for image synthesis by IBR.},
keywords={},
doi={},
ISSN={},
month={January},}
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TY - JOUR
TI - Stereo Matching between Three Images by Iterative Refinement in PVS
T2 - IEICE TRANSACTIONS on Information
SP - 89
EP - 100
AU - Makoto KIMURA
AU - Hideo SAITO
AU - Takeo KANADE
PY - 2003
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E86-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2003
AB - In the field of computer vision and computer graphics, Image-Based-Rendering (IBR) methods are often used to synthesize images from real scene. The image synthesis by IBR requires dense correct matching points in the images. However, IBR does not require 3D geometry reconstruction or camera calibration in Euclidean geometry. On the other hand, 3D reconstructed model can easily point out the occlusion in images. In this paper, we propose an approach to reconstruct 3D shape in a voxel space, which is named Projective Voxel Space (PVS). Since PVS is defined by projective geometry, it requires only weak calibration. PVS is determined by rectifications of the epipolar lines in three images. Three rectified images are orthogonal projected images of a scene in PVS, so processing about image projection is easy in PVS. In both PVS and Euclidean geometry, a point in an image is on a projection from a point on a surface of the object in the scene. Then the other image might have a correct matching point without occlusion, or no matching point because of occlusion. This is a kind of restriction about searching matching points or surface of the object. Taking advantage of simplicity of projection in PVS, the correlation values of points in images are computed, and the values are iteratively refined using the restriction described above. Finally, the shapes of the objects in the scene are acquired in PVS. The reconstructed shape in PVS does not have similarity to 3D shape in Euclidean geometry. However, it denotes consistent matching points in three images, and also indicates the existence of occluded points. Therefore, the reconstructed shape in PVS is sufficient for image synthesis by IBR.
ER -
English
