Plenoptic cameras are increasingly gaining attention in various fields due to their ability to capture both spatial and angular information of light rays. Accurate geometric calibration can lay a solid foundation for the applications that use the plenoptic camera. In this paper, to the best of our knowledge, we first introduce an accurate corner detection method based on a novel selection and refinement strategy. The detected-corner candidates on raw images are selected by a random sample consensus (RANSAC)-based algorithm and optimized by the photometric similarity, as well as the sub-pixel refinement. In addition, a robust and accurate stepwise calibration method is proposed based on separated intrinsic parameters, including parameters related to the pinhole model and those unique to the plenoptic camera. Experiments on both simulated and real data demonstrate that our method outperforms the state-of-the-art methods and is able to support a more accurate calibration of plenoptic cameras. (C) 2020 Optical Society of America
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