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While a traditional camera only captures one point of view of a scene, aplenoptic or light-field camera, is able to capture spatial and angularinformation in a single snapshot, enabling depth estimation from a singleacquisition. In this paper, we present a new metric depth estimation algorithmusing only raw images from a multi-focus plenoptic camera. The proposedapproach is especially suited for the multi-focus configuration where severalmicro-lenses with different focal lengths are used. The main goal of our bluraware depth estimation (BLADE) approach is to improve disparity estimation fordefocus stereo images by integrating both correspondence and defocus cues. Wethus leverage blur information where it was previously considered a drawback.We explicitly derive an inverse projection model including the defocus blurproviding depth estimates up to a scale factor. A method to calibrate theinverse model is then proposed. We thus take into account depth scaling toachieve precise and accurate metric depth estimates. Our results show thatintroducing defocus cues improves the depth estimation. We demonstrate theeffectiveness of our framework and depth scaling calibration on relative depthestimation setups and on real-world 3D complex scenes with ground truthacquired with a 3D lidar scanner.

Blur aware metric depth estimation with multi-focus plenoptic cameras