[1] S Rabia, G Allain, R Tremblay et al. Orthoscopic elemental image synthesis for 3D light field display using lens design software and real-world captured neural radiance field. Opt Express, 32, 7800-7815(2024).

[2] D Charatan, S L Li, A Tagliasacchi et al. pixelSplat: 3D gaussian splats from image pairs for scalable generalizable 3d reconstruction, 19457-19467(2024).

[3] Y L Li, Y Y Chen, Y L Cui et al. LF-UMTI: unsupervised multi-exposure light field image fusion based on multi-scale spatial-angular interaction. Opto-Electron Eng, 51, 240093(2024).

[4] T Q Lv, Y C Wu, X L Zhao. Light field image super-resolution network based on angular difference enhancement. Opto-Electron Eng, 50, 220185(2023).

[5] H G Jeon, J Park, G Choe et al. Accurate depth map estimation from a lenslet light field camera, 1547-1555(2015). https://doi.org/10.1109/CVPR.2015.7298762

[6] T C Wang, A A Efros, R Ramamoorthi. Occlusion-aware depth estimation using light-field cameras, 3487-3495(2015). https://doi.org/10.1109/ICCV.2015.398

[7] S Zhang, H Sheng, C Li et al. Robust depth estimation for light field via spinning parallelogram operator. Comput Vis Image Underst, 145, 148-159(2016).

[8] K Han, W Xiang, E Wang et al. A novel occlusion-aware vote cost for light field depth estimation. IEEE Trans Pattern Anal Mach Intell, 44, 8022-8035(2022).

[9] Y J Tsai, Y L Liu, M Ouhyoung et al. Attention-based view selection networks for light-field disparity estimation, 12095-12103(2020). https://doi.org/10.1609/aaai.v34i07.6888

[10] Y Q Wang, L G Wang, Z Y Liang et al. Occlusion-aware cost constructor for light field depth estimation, 19777-19786(2022). https://doi.org/10.1109/CVPR52688.2022.01919

[11] W T Chao, X C Wang, Y Q Wang et al. Learning sub-pixel disparity distribution for light field depth estimation. IEEE Trans Comput Imaging, 9, 1126-1138(2023).

[12] P P Srinivasan, T Z Wang, A Sreelal et al. Learning to synthesize a 4D RGBD light field from a single image, 2262-2270(2017). https://doi.org/10.1109/ICCV.2017.246

[13] J Y Peng, Z W Xiong, Y C Wang et al. Zero-shot depth estimation from light field using a convolutional neural network. IEEE Trans Comput Imaging, 6, 682-696(2020).

[14] W H Zhou, E C Zhou, G M Liu et al. Unsupervised monocular depth estimation from light field image. IEEE Trans Image Process, 29, 1606-1617(2020).

[15] J Jin, J H Hou. Occlusion-aware unsupervised learning of depth from 4-D light fields. IEEE Trans Image Process, 31, 2216-2228(2022).

[16] S S Zhang, N Meng, E Y Lam. Unsupervised light field depth estimation via multi-view feature matching with occlusion prediction. IEEE Trans Circuits Syst Video Technol, 34, 2261-2273(2024).

[17] C Godard, O M Aodha, G J Brostow. Unsupervised monocular depth estimation with left-right consistency, 6602-6611(2017). https://doi.org/10.1109/CVPR.2017.699

[18] K M He, X Y Zhang, S Q Ren et al. Deep residual learning for image recognition, 770-778(2016). https://doi.org/10.1109/CVPR.2016.90

[19] K Honauer, O Johannsen, D Kondermann et al. A dataset and evaluation methodology for depth estimation on 4D light fields, 19-34(2016). https://doi.org/10.1007/978-3-319-54187-7_2

[20] J L Shi, X R Jiang, C Guillemot. A framework for learning depth from a flexible subset of dense and sparse light field views. IEEE Trans Image Process, 28, 5867-5880(2019).

[22] M Rerabek, T Ebrahimi. New light field image dataset, 1-2(2016).

[23] A S Raj, M Lowney, R Shah et al. Stanford lytro light field archive. http://lightfields.stanford.edu/LF2016.html