Stochastic sampling in computer graphics
ACM Transactions on Graphics (TOG)
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Spatial tessellations: concepts and applications of Voronoi diagrams
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An image-based approach to three-dimensional computer graphics
An image-based approach to three-dimensional computer graphics
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Multiple view geometry in computer visiond
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Computer Vision: A Modern Approach
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SMBV '01 Proceedings of the IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV'01)
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ACM SIGGRAPH 2005 Papers
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Multisensor signal processing: theory and algorithms for image-based rendering and multichannel sampling
High-accuracy stereo depth maps using structured light
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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IEEE Transactions on Multimedia
Spectral analysis for sampling image-based rendering data
IEEE Transactions on Circuits and Systems for Video Technology
Depth and depth-color coding using shape-adaptive wavelets
Journal of Visual Communication and Image Representation
Free-viewpoint depth image based rendering
Journal of Visual Communication and Image Representation
Stereo/multiview picture quality: Overview and recent advances
Image Communication
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We propose a new approach to quantitatively analyze the rendering quality of image-based rendering (IBR) algorithms with depth information. The resulting error bounds for synthesized views depend on IBR configurations including the depth and intensity estimate errors, the scene geometry and texture, the number of actual cameras, their positions and resolution. Specifically, the IBR error is bounded by the summation of three terms, highlighting the impact of using multiple actual cameras, the impact of the noise level at the actual cameras, and the impact of the depth accuracy. We also quantify the impact of occlusions and intensity discontinuities. The proposed methodology is applicable to a large class of common IBR algorithms and can be applied locally. Experiments with synthetic and real scenes show that the developed error bounds accurately characterize the rendering errors. In particular, the error bounds correctly characterize the decay rates of synthesized views' mean absolute errors as O(λ-1 and O(λ-2, where λ is the local density of actual samples, for 2-D and 3-D scenes, respectively. Finally, we discuss the implications of the proposed analysis on camera placement, budget allocation, and bit allocation.