Fast Depth Estimation for Light Field Cameras

Mishiba, Kazu

doi:10.1109/tip.2020.2970814

Cited by 40 publications

(10 citation statements)

References 31 publications

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“…More recently, Mishiba's work [36] focused on devising a fast stereo matching-based depth-estimation algorithm for LF cameras. The main novelty lies in an offline cost volume interpolation, and in a weighted median filter which replaced the usual graph cut algorithm as global optimization solver, thus, increasing the speed of the overall algorithm.…”

Section: Conventional Computer-vision Methodsmentioning

confidence: 99%

Depth Estimation for Light-Field Images Using Stereo Matching and Convolutional Neural Networks

Rogge

Schiopu

Munteanu

2020

Sensors

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The paper presents a novel depth-estimation method for light-field (LF) images based on innovative multi-stereo matching and machine-learning techniques. In the first stage, a novel block-based stereo matching algorithm is employed to compute the initial estimation. The proposed algorithm is specifically designed to operate on any pair of sub-aperture images (SAIs) in the LF image and to compute the pair’s corresponding disparity map. For the central SAI, a disparity fusion technique is proposed to compute the initial disparity map based on all available pairwise disparities. In the second stage, a novel pixel-wise deep-learning (DL)-based method for residual error prediction is employed to further refine the disparity estimation. A novel neural network architecture is proposed based on a new structure of layers. The proposed DL-based method is employed to predict the residual error of the initial estimation and to refine the final disparity map. The experimental results demonstrate the superiority of the proposed framework and reveal that the proposed method achieves an average improvement of 15.65% in root mean squared error (RMSE), 43.62% in mean absolute error (MAE), and 5.03% in structural similarity index (SSIM) over machine-learning-based state-of-the-art methods.

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Section: Conventional Computer-vision Methodsmentioning

confidence: 99%

Depth Estimation for Light-Field Images Using Stereo Matching and Convolutional Neural Networks

Rogge

Schiopu

Munteanu

2020

Sensors

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“…A LTHOUGH light field (LF) cameras enable many attractive functions such as post-capture image editing [1]- [3], depth sensing [4]- [9], saliency detection [10]- [14], and de-occlusion [15]- [17], the resolution of a sub-aperture image (SAI) is much lower than that of the total sensors. The low spatial resolution problem hinders the development of LF imaging [18].…”

Section: Introductionmentioning

confidence: 99%

Light Field Image Super-Resolution Using Deformable Convolution

Wang

Yang

Wang

et al. 2021

IEEE Trans. on Image Process.

108

116

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“…One of the most important tasks of the LF camera is depth estimation [12][13][14][15][16][17][18]. Compared with traditional stereo image pairs, LFs extend disparity to a continuous space.…”

Section: Introductionmentioning

confidence: 99%

“…This advantage is apparent when considering epipolar plane images (EPIs) [19]. Due to a dense sampling in the angular direction (e.g., 15 15 sub-views in Lytro Illum), corresponding pixels of a scene point in sub-aperture images (SAIs) can be projected onto a slope line in EPIs, and line parameters can be encoded into dense stereo matching to obtain a more robust depth estimation [14]. However, a single LF has a weakness which cannot be ignored: the extremely small baseline between SAIs (e.g., 14 in Lytro Illum).…”

Section: Introductionmentioning

confidence: 99%

“…However, a single LF has a weakness which cannot be ignored: the extremely small baseline between SAIs (e.g., 14 in Lytro Illum). With this structural imperfection, even if LF depth estimation methods [12,[15][16][17][18] achieve a high disparity resolution, a low depth resolution is still inevitable, which generally leads to a layered 3D reconstruction, as shown in Fig. 1.…”

Section: Introductionmentioning

confidence: 99%

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Light field Rectification based on relative pose estimation

Xiao¹,

Jin²,

Zhang³

et al. 2022

Preprint

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Hand-held light field (LF) cameras have unique advantages in computer vision such as 3D scene reconstruction and depth estimation. However, the related applications are limited by the ultra-small baseline, e.g., leading to the extremely low depth resolution in reconstruction. To solve this problem, we propose to rectify LF to obtain a large baseline. Specifically, the proposed method aligns two LFs captured by two hand-held LF cameras with a random relative pose, and extracts the corresponding row-aligned sub-aperture images (SAIs) to obtain an LF with a large baseline. For an accurate rectification, a method for pose estimation is also proposed, where the relative rotation and translation between the two LF cameras are estimated. The proposed pose estimation minimizes the degree of freedom (DoF) in the LF-point-LF-point correspondence model and explicitly solves this model in a linear way. The proposed pose estimation outperforms the state-of-the-art algorithms by providing more accurate results to support rectification. The significantly improved depth resolution in 3D reconstruction demonstrates the effectiveness of the proposed LF rectification.

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Fast Depth Estimation for Light Field Cameras

Cited by 40 publications

References 31 publications

Depth Estimation for Light-Field Images Using Stereo Matching and Convolutional Neural Networks

Depth Estimation for Light-Field Images Using Stereo Matching and Convolutional Neural Networks

Light Field Image Super-Resolution Using Deformable Convolution

Light field Rectification based on relative pose estimation

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