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Rendering images with lens distortion that matches real cameras requires a camera model that allows calibration of relevant parameters based on real imagery. This requirement is not fulfilled for camera models typically used in the field of Computer Graphics. In this paper, we present two approaches to integrate realistic lens distortions effects into any graphics pipeline. Both approaches are based on the most widely used camera model in Computer Vision, and thus can reproduce the behavior of real calibrated cameras. The advantages and drawbacks of the two approaches are compared, and both are verified by recovering rendering parameters through a calibration performed on rendered images.

Human Action Recognition based on 3D Convolution Neural Networks from RGBD Videos