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We study 3-D retinal curvature estimation from multiple images that provides the fundamental geometry of the human retina and could be used for 3-D retina visualization and disease diagnosis purposes. An affine camera model is used for 3-D reconstruction due to its simplicity, linearity, and robustness. A major challenge is that a series of optics is involved in the retinal imaging process, including an actual fundus camera, a digital camera, and the optics of the human eye, all of which cause significant nonlinear distortions in retinal images. In this paper, we develop a new constrained optimization method that considers both the geometric shape of the human retina and nonlinear lens distortions. Moreover, we examine a variety of lens distortion models to approximate the optics of the human eye in order to create a smooth spherical surface for curvature estimation. The experimental results on both synthetic data and real retinal images validate the proposed algorithm.