JOURNAL ARTICLE

Image-based rendering using image-based priors

Abstract

Given a set of images acquired from known viewpoints, we describe a method for synthesizing the image which would be seen from a new viewpoint. In contrast to existing techniques, which explicitly reconstruct the 3D geometry of the scene, we transform the problem to the reconstruction of colour rather than depth. This retains the benefits of geometric constraints, but projects out the ambiguities in depth estimation which occur in textureless regions. On the other hand, regularization is still needed in order to generate high-quality images. The paper's second contribution is to constrain the generated views to lie in the space of images whose texture statistics are those of the input images. This amounts to an image-based prior on the reconstruction which regularizes the solution, yielding realistic synthetic views. Examples are given of new view generation for cameras interpolated between the acquisition viewpoints - which enables synthetic steadicam stabilization of a sequence with a high level of realism.

Keywords:
Artificial intelligence Computer vision Rendering (computer graphics) Computer science Prior probability View synthesis Regularization (linguistics) Iterative reconstruction Image-based modeling and rendering Image (mathematics) Viewpoints Bayesian probability

Metrics

157
Cited By
12.83
FWCI (Field Weighted Citation Impact)
32
Refs
0.99
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Advanced Vision and Imaging
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Advanced Image Processing Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Computer Graphics and Visualization Techniques
Physical Sciences →  Computer Science →  Computer Graphics and Computer-Aided Design

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