Fast and Accurate Micro Lenses Depth Maps for Multi-focus Light Field Cameras

Ferreira, Rita; Gonçalves, Nuno

doi:10.1007/978-3-319-45886-1_25

Cited by 6 publications

(7 citation statements)

References 10 publications

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“…During the actual process of calculating refocused images or total focus images, most methods [9,[13][14][15][16][17] use the center of micro-image to approximate the center of micro-lens. Essentially, this is equivalent to refocusing based on Equation (5) other than Equation (1).…”

Section: Comparison Of Refocusing Algorithmmentioning

confidence: 99%

“…However, the methods mentioned [6][7][8] are designed for specific feature pattern, which is hard to use in SFM. Ferreira et al [9] use the scale-invariant feature transform (SIFT) detector [10] to extract features and calculate depth on the raw image. However, the small size of micro-image makes the results less robust and accurate.…”

Section: Introductionmentioning

confidence: 99%

“…Although it is easier to extract features from the total focus image, the traditional refocusing algorithm of focused plenoptic camera has some limitations. The traditional refocusing algorithm is based on the concept of virtual depth [2], which is widely used in the calculation of depth maps and total focus image [9,[13][14][15][16][17]. However, the theoretical imaging range of the refocused image based on traditional method [2] is limited by the sensor size and the traditional focal stack do not conform to the central projection in theory.…”

Section: Introductionmentioning

confidence: 99%

“…However, the theoretical imaging range of the refocused image based on traditional method [2] is limited by the sensor size and the traditional focal stack do not conform to the central projection in theory. Besides, most methods [9,[13][14][15][16][17] ignore the difference between the center of micro-lens and micro-image when calculating depth maps and total focus images, which will result in the deviation of calculated results from the theoretical model.…”

Section: Introductionmentioning

confidence: 99%

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Feature Detection of Focused Plenoptic Camera Based on Central Projection Stereo Focal Stack

et al. 2020

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Fast and accurate feature extraction can lay a solid foundation for scene reconstruction and visual odometry. However, this has been rather a difficult problem for the focused plenoptic camera. In this paper, to the best of our knowledge, we first introduce an accurate and fast feature extraction algorithm based on central projection stereo focal stack (CPSFS). Specifically, we propose a refocusing algorithm that conforms to the central projection with regard to the center of main lens, which is more accurate than traditional one. On this basis, the feature is extracted on the CPSFS without calculating dense depth maps and total focus images. We verify the precision and efficiency of the proposed algorithm through simulated and real experiments, and give an example of scene reconstruction based on the proposed method. The experimental results show that our feature extraction algorithm is able to support the feature-based scene reconstruction via focused plenoptic camera.

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Section: Comparison Of Refocusing Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Feature Detection of Focused Plenoptic Camera Based on Central Projection Stereo Focal Stack

et al. 2020

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“…Stereo correspondences can also be directly reprojected into a three-dimensional point cloud, as done in 4. Disparity Estimation using Plenoptic Cameras [FG16], yet the accuracy of the results do not differs significantly with the respect to the disparity calculated on the raw plenoptic images.…”

Section: State Of the Artmentioning

confidence: 78%

Efficient and Accurate Disparity Estimation from MLA-Based Plenoptic Cameras

Koch¹

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Microlens-array based plenoptic cameras capture the light field in a single shot, enabling new potential applications but also introducing additional challenges. A plenoptic image consists of thousand of microlens images. Estimating the disparity for each microlens allows to render conventional images, changing the perspective and the focal settings, and to reconstruct the three-dimensional geometry of the scene. The work includes a blur-aware calibration method to model plenoptic cameras, an optimization method to accurately select the best microlenses combination for disparity estimation, an overview of the different types of plenoptic cameras, an analysis of the disparity estimation algorithms, and a robust depth estimation approach for light field microscopy. The research led to the creation of a full framework for plenoptic cameras, which contains the implementation of the algorithms discussed in the work and datasets of both real and synthetic images for comparison, benchmarking and future research.

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Optimizing the Lens Selection Process for Multi-focus Plenoptic Cameras and Numerical Evaluation

Palmieri

Koch

2017

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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Fast and Accurate Micro Lenses Depth Maps for Multi-focus Light Field Cameras

Cited by 6 publications

References 10 publications

Feature Detection of Focused Plenoptic Camera Based on Central Projection Stereo Focal Stack

Feature Detection of Focused Plenoptic Camera Based on Central Projection Stereo Focal Stack

Efficient and Accurate Disparity Estimation from MLA-Based Plenoptic Cameras

Optimizing the Lens Selection Process for Multi-focus Plenoptic Cameras and Numerical Evaluation

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