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3D RECONSTRUCTION USING PHOTO CONSISTENCY FROM
UNCALIBRATED MULTIPLE VIEWS
Heewon Lee and Alper Yilmaz
Photommetry Computer Vision Lab, Ohio State University, Columbus, OH 43210, U.S.A.
Keywords:
Photo consistency, Homography, 3D Recovery.
Abstract:
This paper presents a new 3D object shape reconstruction approach, which exploits the homography transform
and photo consistency between multiple images. The proposed method eliminates the requirement of dense
feature correspondences, camera calibration, and pose estimation. Using planar homography, we generate
a set of planes slicing the object to a set of parallel cross-sections in the 3D object space. For each object
slice, we check photo consistency based on color observation. This approach in return provides us with the
capability for expressing convex and concave parts of the object. We show that the application of our approach
to a standard multiple view dataset achieves comparably better performance than competing silhouette based
method.
1 INTRODUCTION
Three-dimensional (3D) reconstruction of the object
shape from multiple images has an important role in
many applications including: tracking (Yilmaz et al,
2006), action recognition (Yilmaz and Shah, 2008)
and virtual reality applications (Bregler, Hertzmann,
and Biermann, 2000). The recent availability of im-
age collection on the Internet and publicly available
applications such as Google Earth and Microsoft Pho-
tosynth has increased the popularity of research on 3D
recovery.
Several approaches achieve 3D shape recovery by
finding 3D locations of matching points in differ-
ent view images using camera calibration and pose
(Isidoro and Sclaroff, 2003). These methods require
a triangulation step, which backprojects points from
the image space to the object space. However, these
methods explicitly require camera pose and calibra-
tion require establishing high number of point corre-
spondences between the images. Due to perspective
distortions, variations of color across different views,
and different camera gains generate a high number of
correspondences.
In contrast, using segmented objects in the images
and their direct back-projections, which generate a vi-
sual hull (Slabaugh and Schafer, 2001) is more flexi-
ble and eliminates the requirement to establish point
correspondences. However, back-projections from
the image space to the object space still require cam-
era pose and calibration for all the images. Another
limitation of back-projection of the silhouette is that
recovered 3D object will not contain details of objects
such as convexities and concavities despite having a
high number of images. To overcome the limitations
of visual hull based methods, researchers have pro-
posed voxel coloring technique, which measures the
color consistency of projected 3D points to images
(Seitz and Szeliski, 2006). These methods label each
grid on the object space as opaque or transparent by
projecting each voxel to input images. These projec-
tions after check the consistency of the projected col-
ors. Each consistent voxel is then checked for visibil-
ity. These methods, however, need precise calibration
and pose information and may result in wrong sur-
faces due to occlusion problems in the visibility test.
In this paper, we propose new technique that elim-
inates the requirements for known camera calibration
and pose. Proposed object approach analyzes implicit
scene and camera geometry through a minimal num-
ber of point correspondences across images, which
are used to form virtual images of a series of hypo-
thetical planes and their relations in the images space.
This paper is an extension to our former paper us-
ing silhouette-based methods(Lai and Yilmaz, 2008),
which improves the recovered 3D considerably by in-
troducing the photo consistency check. Particularly,
the photo consistency check introduced in this paper
provides detailed 3D recovery of convexities and con-
cavities in the object surface.
484
Lee H. and Yilmaz A. (2010).
3D RECONSTRUCTION USING PHOTO CONSISTENCY FROM UNCALIBRATED MULTIPLE VIEWS.
In Proceedings of the International Conference on Computer Vision Theory and Applications, pages 484-487
DOI: 10.5220/0002850504840487
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