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Leveraging blur information in plenoptic cameras: Application to calibration and metric depth estimation

Abstract : This thesis investigates the use of a vision sensor called a plenoptic camera for computer vision in robotics applications. To achieve this goal we place ourselves upstream of applications, and focus on its modelization to enable robust depth estimation. Plenoptic or light-field cameras are passive imaging systems able to capture both spatial and angular information about a scene in a single exposure. These systems are usually built upon a micro-lenses array (MLA) placed between a main lens and a sensor. Their design enables depth estimation from a single acquisition. The key contributions of this work lie in answering the questions "How can we link world space information to the image space information?" and more importantly, "How can we link image space information to world space information?". We address the first problem through the prism of calibration, by proposing a new camera model and a methodology to retrieve the intrinsic parameters of this model. We leverage blur information where it was previously considered as a drawback by explicitly modeling the defocus blur. We address the second one as the problematic of depth estimation, by proposing a metric depth estimation framework working directly with raw plenoptic images. It takes into account both correspondence and defocus cues. Our model generalizes to various configurations, including the multi-focus plenoptic camera (both in Galilean and Keplerian configuration), as well as to the single-focus and unfocused plenoptic camera. Our method gives accurate and precise depth estimates (a median relative error ranging from 1.27% to 4.75% of the distance). It outperforms state-of-the-art methods. Having a new complete camera model and enabling robust metric depth estimation from raw images only, opens the door to many new applications. It is an additional step towards practical use of plenoptic cameras in computer vision applications.
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Contributor : Mathieu Labussiere Connect in order to contact the contributor
Submitted on : Thursday, February 3, 2022 - 10:52:45 AM
Last modification on : Thursday, February 10, 2022 - 3:35:29 AM
Long-term archiving on: : Wednesday, May 4, 2022 - 6:31:19 PM


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  • HAL Id : tel-03554252, version 1


Mathieu Labussière. Leveraging blur information in plenoptic cameras: Application to calibration and metric depth estimation. Computer Vision and Pattern Recognition [cs.CV]. Université Clermont Auvergne, 2021. English. ⟨tel-03554252⟩



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