• Laser & Optoelectronics Progress
  • Vol. 59, Issue 16, 1611002 (2022)
Jiatao Liu1, Yaping Zhang1、*, and Yuwei Yang2
Author Affiliations
  • 1School of Information Science and Technology, Yunnan Normal University, Kunming 650500, Yunnan , China
  • 2Nantong Institute of Technology, Nantong 226000, Jiangsu , China
  • show less
    DOI: 10.3788/LOP202259.1611002 Cite this Article Set citation alerts
    Jiatao Liu, Yaping Zhang, Yuwei Yang. Efficient Monocular Image Depth Estimation Based on Transfer Learning[J]. Laser & Optoelectronics Progress, 2022, 59(16): 1611002 Copy Citation Text show less
    References

    [1] Wang Y T, Zhou H Q, Yan J X et al. Advances in computational optics based on deep learning[J]. Chinese Journal of Lasers, 48, 1918004(2021).

    [2] Ding M, Jiang X Y. Scene depth estimation based on monocular vision in advanced driving assistance system[J]. Acta Optica Sinica, 40, 1715001(2020).

    [3] Kang C, Li W X, Huang S et al. Research on active optical correction algorithm based on deep learning[J]. Acta Optica Sinica, 41, 0611004(2021).

    [4] Alhashim I, Wonka P. High quality monocular depth estimation via transfer learning[EB/OL]. https://arxiv.org/abs/1812.11941

    [5] Eigen D, Puhrsch C, Fergus R. Depth map prediction from a single image using a multi-scale deep network[EB/OL]. https://arxiv.org/abs/1406.2283

    [6] Laina I, Rupprecht C, Belagiannis V et al. Deeper depth prediction with fully convolutional residual networks[C], 239-248(2016).

    [7] Bhat S F, Alhashim I, Wonka P. AdaBins: depth estimation using adaptive bins[C], 4008-4017(2021).

    [8] Fu H, Gong M M, Wang C H et al. Deep ordinal regression network for monocular depth estimation[C], 2002-2011(2018).

    [9] Li J, Klein R, Yao A. A two-streamed network for estimating fine-scaled depth maps from single RGB images[C], 3372-3380(2017).

    [10] Wu J L, Guo Z H, Chen X F et al. Three-dimensional measurement method of light field imaging based on deep learning[J]. Chinese Journal of Lasers, 47, 1204005(2020).

    [11] Ranftl R, Bochkovskiy A, Koltun V. Vision transformers for dense prediction[C], 12159-12168(2021).

    [12] Dosovitskiy A, Beyer L, Kolesnikov A et al. An image is worth 16x16 words: transformers for image recognition at scale[EB/OL]. https://arxiv.org/abs/2010.11929

    [13] Vaswani A, Shazeer N, Parmar N et al. Attention is all you need[C], 5998-6008(2017).

    [14] Lin G S, Milan A, Shen C H et al. RefineNet: multi-path refinement networks for high-resolution semantic segmentation[C], 5168-5177(2017).

    [15] Wang Z, Simoncelli E P, Bovik A C. Multiscale structural similarity for image quality assessment[C], 1398-1402(2003).

    [16] Fan H Q, Su H, Guibas L. A point set generation network for 3D object reconstruction from a single image[C], 2463-2471(2017).

    [17] Levin A, Lischinski D, Weiss Y. Colorization using optimization[C], 689-694(2004).

    [18] Paszke A, Gross S, Massa F et al. PyTorch: an imperative style, high-performance deep learning library[C], 8024-8035(2019).

    [19] Kingma D P, Ba J. Adam: a method for stochastic optimization[EB/OL]. https://arxiv.org/abs/1412.6980

    [20] Deng J, Dong W, Socher R et al. ImageNet: a large-scale hierarchical image database[C], 248-255(2009).

    [21] Sun C, Shrivastava A, Singh S et al. Revisiting unreasonable effectiveness of data in deep learning era[C], 843-852(2017).

    [22] Glorot X, Bengio Y. Understanding the difficulty of training deep feedforward neural networks[C], 249-256(2010).

    Jiatao Liu, Yaping Zhang, Yuwei Yang. Efficient Monocular Image Depth Estimation Based on Transfer Learning[J]. Laser & Optoelectronics Progress, 2022, 59(16): 1611002
    Download Citation