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Parameterized Variety Based View Synthesis Scheme for Multi-view 3DTV

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Computer Vision – ACCV 2012 (ACCV 2012)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7727))

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Abstract

This paper presents a novel parameterized variety based view synthesis scheme for 3DTV and multi-view systems. We have generalized the parameterized image variety approach to image based rendering proposed in [1] to handle full perspective cameras. An algebraic geometry framework is proposed for the parameterization of the variety associated with full perspective images, by image positions of three reference scene points. A complete parameterization of the 3D scene is constructed. This allows to generate realistic novel views from arbitrary viewpoints without explicit 3D reconstruction, taking few multi-view images as input from uncalibrated cameras.

Another contribution of this paper is to provide a generalised and flexible architecture based on this variety model for multi-view 3DTV. The novelty of the architecture lies in merging this variety based approach with standard depth image based view synthesis pipeline, without explicitly obtaining sparse or dense 3D points. This integrated framework subsequently overcomes the problems associated with existing depth based representations. The key aspects of this joint framework are: 1) Synthesis of artifacts free novel views from arbitrary camera positions for wide angle viewing. 2) Generation of signal representation compatible with standard multi-view systems. 3) Extraction of reliable view dependent depth maps from arbitrary virtual viewpoints without recovering exact 3D points. 4) Intuitive interface for virtual view specification based on scene content. Experimental results on standard multi-view sequences are presented to demonstrate the effectiveness of the proposed scheme.

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Sharma, M., Chaudhury, S., Lall, B. (2013). Parameterized Variety Based View Synthesis Scheme for Multi-view 3DTV. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds) Computer Vision – ACCV 2012. ACCV 2012. Lecture Notes in Computer Science, vol 7727. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37447-0_41

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  • DOI: https://doi.org/10.1007/978-3-642-37447-0_41

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37446-3

  • Online ISBN: 978-3-642-37447-0

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