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  • Parametric Manifold of an Object under Different Viewing Directions

    Author(s)
    Zhang, Xiaozheng
    Gao, Yongsheng
    Caelli, Terry
    Griffith University Author(s)
    Caelli, Terrence M.
    Gao, Yongsheng
    Zhang, Paul
    Year published
    2012
    Metadata
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    Abstract
    The appearance of a 3D object depends on both the viewing directions and illumination conditions. It is proven that all n-pixel images of a convex object with Lambertian surface under variable lighting from infinity form a convex polyhedral cone (called illumination cone) in n-dimensional space. This paper tries to answer the other half of the question: What is the set of images of an object under all viewing directions? A novel image representation is proposed, which transforms any n-pixel image of a 3D object to a vector in a 2n-dimensional pose space. In such a pose space, we prove that the transformed images of a 3D ...
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    The appearance of a 3D object depends on both the viewing directions and illumination conditions. It is proven that all n-pixel images of a convex object with Lambertian surface under variable lighting from infinity form a convex polyhedral cone (called illumination cone) in n-dimensional space. This paper tries to answer the other half of the question: What is the set of images of an object under all viewing directions? A novel image representation is proposed, which transforms any n-pixel image of a 3D object to a vector in a 2n-dimensional pose space. In such a pose space, we prove that the transformed images of a 3D object under all viewing directions form a parametric manifold in a 6-dimensional linear subspace. With in-depth rotations along a single axis in particular, this manifold is an ellipse. Furthermore, we show that this parametric pose manifold of a convex object can be estimated from a few images in different poses and used to predict object's appearances under unseen viewing directions. These results immediately suggest a number of approaches to object recognition, scene detection, and 3D modelling. Experiments on both synthetic data and real images were reported, which demonstrates the validity of the proposed representation.
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    Conference Title
    COMPUTER VISION - ECCV 2012, PT V
    Volume
    7576
    Issue
    PART 5
    Publisher URI
    http://eccv2012.unifi.it/
    DOI
    https://doi.org/10.1007/978-3-642-33715-4_14
    Subject
    Computer vision
    Publication URI
    http://hdl.handle.net/10072/52334
    Collection
    • Conference outputs

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