• Laser & Optoelectronics Progress
  • Vol. 59, Issue 14, 1415014 (2022)
Kunliang Xie1, Renjiao Yi1, Haifang Zhou1, Chenyang Zhu1..., Yuwan Liu2 and Kai Xu1,*|Show fewer author(s)
Author Affiliations
  • 1School of Computer Science, National University of Defense Technology, Changsha 410005, Hunan , China
  • 2Dongbu Zhanqu Zhanqinju, Nanjing 210000, Jiangsu , China
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    DOI: 10.3788/LOP202259.1415014 Cite this Article Set citation alerts
    Kunliang Xie, Renjiao Yi, Haifang Zhou, Chenyang Zhu, Yuwan Liu, Kai Xu. Efficient Material Editing of Single Image Based on Inverse Rendering[J]. Laser & Optoelectronics Progress, 2022, 59(14): 1415014 Copy Citation Text show less
    Architecture of proposed neural network
    Fig. 1. Architecture of proposed neural network
    Picture display in the Hierarchical Shininess dataset
    Fig. 2. Picture display in the Hierarchical Shininess dataset
    Visual comparisons of highlight separation on real pictures
    Fig. 3. Visual comparisons of highlight separation on real pictures
    Visual comparisons on the MIT intrinsics dataset
    Fig. 4. Visual comparisons on the MIT intrinsics dataset
    Visual comparisons on specular layer between GT images and network output images
    Fig. 5. Visual comparisons on specular layer between GT images and network output images
    Visual comparisons between the synthetic images (GT) after the combination of specular layer and diffuse layer with different material shininess parameters and the output results of the proposed method
    Fig. 6. Visual comparisons between the synthetic images (GT) after the combination of specular layer and diffuse layer with different material shininess parameters and the output results of the proposed method
    Material editing of real pictures
    Fig. 7. Material editing of real pictures
    NetworkBatch sizeOptimizerInitial learning rateNumber of iterations
    Specular-Net64Adam1×10-450×103
    IID-Net64Adam1×10-428×103
    Shininess-Net64Adam1×10-435×103
    Table 1. Hyperparameter settings in network training
    NetworkDatasetSourceSize
    Specular-NetLIMEMeka et al.[4]8.5×104
    IID-NetShapeNet IntrinsicsShi et al.[5]20×104
    Shininess-NetHierarchical ShininessOurs140×104
    Table 2. Datasets used by proposed networks
    MethodSHIQ datasetShapeNet Intrinsics datasetReal pictures
    SMSELMSEDSSIMSMSELMSEDSSIMSMSELMSEDSSIM
    Shen13280.05870.3330.20020.12860.24330.2070.02240.1670.1745
    Shi1750.03050.17890.21990.01330.10560.15760.01670.24970.2087
    Souza1890.06070.3490.20620.12580.23910.21030.0090.11780.1639
    Yamamoto19290.05940.33640.20110.12860.24180.2060.02250.16670.1743
    Fu21100.00030.00190.00880.01070.03320.10340.00560.10350.1414
    Ours0.01080.04850.17650.00780.04490.09270.00560.14480.1384
    Table 3. Quantitative comparisons on the SHIQ dataset, ShapeNet Intrinsics dataset, and some real pictures
    MethodSMSELMSEDSSIM
    albedoshadingalbedoshadingalbedoshading
    SIRFS15[30]0.01470.00830.04160.01680.12380.0985
    DI15150.02770.01540.05850.02950.15260.1328
    Shi1750.02780.01260.05030.02400.14650.1200
    Yi20190.02740.01450.04760.0284
    SMCH21310.02250.01460.04840.02780.14990.1912
    Ours0.02730.01290.03860.03350.14840.1420
    Table 4. Quantitative comparisons on the MIT intrinsics dataset
    Kunliang Xie, Renjiao Yi, Haifang Zhou, Chenyang Zhu, Yuwan Liu, Kai Xu. Efficient Material Editing of Single Image Based on Inverse Rendering[J]. Laser & Optoelectronics Progress, 2022, 59(14): 1415014
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