Abstract
Photonic mixer device (PMD) range cameras are becoming popular as an alternative to algorithmic 3D reconstruction but their main drawbacks are low-resolution (LR) and noise. Recently, some interesting works have stressed on resolution enhancement of PMD range data. These works use high-resolution (HR) CCD images or stereo pairs. But such a system requires complex setup and camera calibration. In contrast, we propose a super-resolution method through induced camera motion to create a HR range image from multiple LR range images. We follow a Bayesian framework by modeling the original HR range as a Markov random field (MRF). To handle discontinuities, we propose the use of an edge-adaptive MRF prior. Since such a prior renders the energy function non-convex, we minimize it by graduated non-convexity.
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