We propose a vector space approach for relighting a Lambertian convex object with distant light source, whose
crucial task is the decomposition of the reflectance function into albedos (or reflection coefficients) and lightings
based on a set of images of the same object and its 3-D model. Making use of the fact that reflectance functions
are well approximated by a low-dimensional linear subspace spanned by the first few spherical harmonics, this
inverse problem can be formulated as a matrix factorization, in which the basis of the subspace is encoded in
the spherical harmonic matrix S. A necessary and sufficient condition on S for unique factorization is derived
with an introduction to a new notion of matrix rank called nonseparable full rank. An SVD-based algorithm for
exact factorization in the noiseless case is introduced. In the presence of noise, the algorithm is slightly modified
by incorporating the positivity of albedos into a convex optimization problem. Implementations of the proposed
algorithms are done on a set of synthetic data.
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