[1] Adelson E H. The plenoptic function and the elements of early vision[J]. Computational Models of Visual Processing, 3-20(1991).

[2] Levoy M, Hanrahan P. Light field rendering[C], 31-42(1996).

[3] Ren N, Levoy M, Bredif M et al. Light field photography with a hand-held plenoptic camera[J]. Stanford University Computer Science Tech Report, 2, 1-11(2005).

[4] Nousias S, Lourakis M, Keane P et al. A linear approach to absolute pose estimation for light fields[C], 672-681(2021).

[5] Fu K R, Jiang Y, Ji G P et al. Light field salient object detection: a review and benchmark[J]. Computational Visual Media, 8, 509-534(2022).

[6] Suhail M, Esteves C, Sigal L et al. Light field neural rendering[C], 8259-8269(2022).

[7] Wu Z A, Zhu X Y, Li J et al. Volumetric-calibration ray tracing-based calculation method of weight coefficient in light field PIV[J]. Acta Optica Sinica, 41, 2010001(2021).

[8] Fang L, Dai Q H. Computational light field imaging[J]. Acta Optica Sinica, 40, 0111001(2020).

[9] Yin Y K, Yu K, Yu C Z et al. 3D imaging using geometric light field: a review[J]. Chinese Journal of Lasers, 48, 1209001(2021).

[10] Yu Z, Guo X, Lin H et al. Line assisted light field triangulation and stereo matching[C], 2792-2799(2013).

[11] Tao M W, Hadap S, Malik J et al. Depth from combining defocus and correspondence using light-field cameras[C], 673-680(2014).

[12] Zhang S, Sheng H, Li C et al. Robust depth estimation for light field via spinning parallelogram operator[J]. Computer Vision and Image Understanding, 145, 148-159(2016).

[13] Johannsen O, Sulc A, Goldluecke B. What sparse light field coding reveals about scene structure[C], 3262-3270(2016).

[14] Heber S, Pock T. Convolutional networks for shape from light field[C], 3746-3754(2016).

[15] Shin C, Jeon H G, Yoon Y et al. EPINET: a fully-convolutional neural network using epipolar geometry for depth from light field images[C], 4748-4757(2018).

[16] Tsai Y J, Liu Y L, Ouhyoung M et al. Attention-based view selection networks for light-field disparity estimation[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 34, 12095-12103(2020).

[17] Wang Y Q, Wang L G, Liang Z Y et al. Occlusion-aware cost constructor for light field depth estimation[C], 19777-19786(2022).

[18] Honauer K, Johannsen O, Kondermann D et al. A dataset and evaluation methodology for depth estimation on 4D light fields[M]. Lai S H, Lepetit V, Nishino K, et al. Computer vision–ACCV 2016, 10113, 19-34(2017).

[19] Wang Q L, Wu B G, Zhu P F et al. ECA-net: efficient channel attention for deep convolutional neural networks[C], 11531-11539(2020).

[20] Strecke M, Alperovich A, Goldluecke B. Accurate depth and normal maps from occlusion-aware focal stack symmetry[C], 2529-2537(2017).

[21] Williem, Park I K, Lee K M. Robust light field depth estimation using occlusion-noise aware data costs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40, 2484-2497(2018).

[22] Zhou W H, Liang L K, Zhang H et al. Scale and orientation aware EPI-patch learning for light field depth estimation[C], 2362-2367(2018).

[23] Schilling H, Diebold M, Rother C et al. Trust your model: light field depth estimation with inline occlusion handling[C], 4530-4538(2018).

[24] Huang Z C, Hu X M, Xue Z et al. Fast light-field disparity estimation with multi-disparity-scale cost aggregation[C], 6300-6309(2022).

[25] Han K, Xiang W, Wang E et al. A novel occlusion-aware vote cost for light field depth estimation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44, 8022-8035(2022).