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Article Dans Une Revue Computer Vision and Image Understanding Année : 2023

Blur aware metric depth estimation with multi-focus plenoptic cameras

Mathieu Labussière
Céline Teulière
Omar Ait Aider

Résumé

While a traditional camera only captures one point of view of a scene, a plenoptic or light-field camera, is able to capture spatial and angular information in a single snapshot, enabling depth estimation from a single acquisition. In this paper, we present a new metric depth estimation algorithm using only raw images from a multi-focus plenoptic camera. The proposed approach is especially suited for the multi-focus configuration where several micro-lenses with different focal lengths are used. The main goal of our blur aware depth estimation (BLADE) approach is to improve disparity estimation for defocus stereo images by integrating both correspondence and defocus cues. We thus leverage blur information where it was previously considered a drawback. We explicitly derive an inverse projection model including the defocus blur providing depth estimates up to a scale factor. A method to calibrate the inverse model is then proposed. We thus take into account depth scaling to achieve precise and accurate metric depth estimates. Our results show that introducing defocus cues improves the depth estimation. We demonstrate the effectiveness of our framework and depth scaling calibration on relative depth estimation setups and on real-world 3D complex scenes with ground truth acquired with a 3D lidar scanner.
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hal-04178873 , version 1 (08-08-2023)

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Paternité - Partage selon les Conditions Initiales

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Mathieu Labussière, Céline Teulière, Omar Ait Aider. Blur aware metric depth estimation with multi-focus plenoptic cameras. Computer Vision and Image Understanding, In press, ⟨10.1016/j.cviu.2023.103802⟩. ⟨hal-04178873⟩
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