Depth range accuracy for plenoptic cameras

Monteiro, Nuno Barroso; Marto, Simão; Barreto, João Pedro; Gaspar, José António

doi:10.1016/j.cviu.2018.01.010

Cited by 18 publications

(7 citation statements)

References 13 publications

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“…And finally the interference comes in to picture when they start operating in bulk amount in indoor and outdoor environments. Further in order to evaluate depth range accuracy, Plenoptic cameras [15][16][17] design came in to existence. This camera uses not only the intensity of light in the scene but also the directional information of light distribution in scene for retrieving depth of the surface.…”

Section: Literature Surveymentioning

confidence: 99%

Depth perception in single rgb camera system using lens aperture and object size: a geometrical approach for depth estimation

Alphonse

Sriharsha

2021

SN Appl. Sci.

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In recent years, with increase in concern about public safety and security, human movements or action sequences are highly valued when dealing with suspicious and criminal activities. In order to estimate the position and orientation related to human movements, depth information is needed. This is obtained by fusing data obtained from multiple cameras at different viewpoints. In practice, whenever occlusion occurs in a surveillance environment, there may be a pixel-to-pixel correspondence between two images captured from two cameras and, as a result, depth information may not be accurate. Moreover use of more than one camera exclusively adds burden to the surveillance infrastructure. In this study, we present a mathematical model for acquiring object depth information using single camera by capturing the in focused portion of an object from a single image. When camera is in-focus, with the reference to camera lens center, for a fixed focal length for each aperture setting, the object distance is varied. For each aperture reading, for the corresponding distance, the object distance (or depth) is estimated by relating the three parameters namely lens aperture radius, object distance and object size in image plane. The results show that the distance computed from the relationship approximates actual with a standard error estimate of 2.39 to 2.54, when tested on Nikon and Cannon versions with an accuracy of 98.1% at 95% confidence level.

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Section: Literature Surveymentioning

confidence: 99%

Depth perception in single rgb camera system using lens aperture and object size: a geometrical approach for depth estimation

Alphonse

Sriharsha

2021

SN Appl. Sci.

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“…Recently, a LF camera can be used to obtain richer information of a scene, such as 3-D reconstruction [7], [8], instead of usual 2-D cameras. The information obtained by the LF camera is inherently equivalent to a set of images captured from different viewpoints, so-called sub-aperture images (SAI).…”

Section: B Related Workmentioning

confidence: 99%

Reliable Planar Object Pose Estimation in Light Fields From Best Subaperture Camera Pairs

Crombez

Caron

Funatomi

et al. 2018

IEEE Robot. Autom. Lett.

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A light-field camera can obtain richer information about a scene than a usual camera. This property offers a lot of potential for robot vision. In this paper, we present a method for pose estimation of a planar object with a light-field camera. The light-field camera can be regarded as a set of sub-aperture cameras. Although any combination of them can theoretically be used for the pose estimation, the accuracy depends on the combination. We show that the estimated pose error can be reduced by selecting the best pair of sub-aperture cameras. We have evaluated the accuracy of our approach with real experiments using a light-field camera in front of planar targets held by an industrial manipulator for ground truth comparison.

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“…Thus, there is a demand for acquiring high-resolution real data efficiently whilst having to reduce inaccuracies in real data acquisition because this will later determine the ground truth data. The Lytro camera has shown promise for estimating high resolution depth for objects close to the camera in both indoor and outdoor settings [9,36,80]. We use the Lytro camera to capture 4D light field images of high-resolution.…”

Section: Limited Real Datamentioning

confidence: 99%

“…al. identified the optimal focal length in light field cameras [80]. The focus depth ranged from 0m to 2m in increments of 0.05m.…”

Section: Deblurringmentioning

confidence: 99%

“…The focus depth ranged from 0m to 2m in increments of 0.05m. Monteiro et al determined that the accuracy of the Lytro Illum camera reduces when the focal length of the main lens is set to be greater than 1.0m [80]. This impacts feature detection because the features on object's surfaces can only be displayed accurately when the objects are too far from the camera.…”

Section: Deblurringmentioning

confidence: 99%

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Accurate dense depth from light field technology for object segmentation and 3D computer vision

San¹

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Depth estimation affects feature extraction, object segmentation and three-dimensional (3D) reconstruction. Practical applications impacted by depth estimation include autonomous vehicle navigation, computational photography, augmented and visual reality. Recent advancements in neural network algorithms have been linked to an increase in depth estimation accuracy. With the aid of graphics processing units (GPU), examples where neural networks have been used for improving depth estimation accuracy are depth from video, multiple images, stereo image pairs or a single image.Despite the emphasis for neural networks to improve depth estimation algorithms, the datasets used in experimental evaluations have not seen the same amount of attention. The benchmark datasets for depth estimation may be lacking in efficiency for obtaining real data, high-resolution images or scenes containing complex object structures. Consequently, this results in many state-of-the-art depth estimation algorithms to fail in practical applications since these scenarios are not considered during the training procedure.Recent state-of-the-art depth estimation algorithms declare that their methodologies are not suitable for occlusion or non-Lambertian surfaces. The Lambertian approximation is defined by two cases.The first case is different viewpoints from multiple cameras are photo-consistent. The second case is objects are composed by a collection of piecewise, planar surfaces. Since many objects in reality are not bound by these two cases, algorithms assuming Lambertian approximations produce low accuracy in practical applications.In this thesis, we propose a cost-efficient, high-resolution dataset that contains scenes of challenging object shapes. The dataset is acquired using light field technology for depth estimation from a Lytro camera. The proposed dataset contains high-resolution images with objects addressing the Lambertian approximation problem. This dataset aims to improve the accuracy of current depth estimation algorithms by instigating difficult evaluations observed in real-world scenarios. The depth information used for ground truth data in our proposed dataset is adapted from the Lytro software for converting the four dimensional (4D) light field file into the isolated depth channel. In comparison to benchmark datasets, this is a cost-effective solution for acquiring real data.We also propose a detailed study and utilisation of generative adversarial networks to predict depth from a single view. During the training procedure of the generative adversarial network, a loss function is optimized for the purpose of depth prediction from a single image. We analyse the generative adversarial method for depth from a single image on the proposed depth dataset in addition to benchmark depth datasets.

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Depth range accuracy for plenoptic cameras

Cited by 18 publications

References 13 publications

Depth perception in single rgb camera system using lens aperture and object size: a geometrical approach for depth estimation

Depth perception in single rgb camera system using lens aperture and object size: a geometrical approach for depth estimation

Reliable Planar Object Pose Estimation in Light Fields From Best Subaperture Camera Pairs

Accurate dense depth from light field technology for object segmentation and 3D computer vision

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