• Laser & Optoelectronics Progress
  • Vol. 60, Issue 16, 1615009 (2023)
Guangzhao Zhu, Bo Wei*, Afeng Yang, and Xin Xu
Author Affiliations
  • School of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310037, Zhejiang, China
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    DOI: 10.3788/LOP222692 Cite this Article Set citation alerts
    Guangzhao Zhu, Bo Wei, Afeng Yang, Xin Xu. Multi-View 3D Reconstruction Method Based on Self-Attention Mechanism[J]. Laser & Optoelectronics Progress, 2023, 60(16): 1615009 Copy Citation Text show less
    Overall structure of SA-PatchmatchNet
    Fig. 1. Overall structure of SA-PatchmatchNet
    Internal structure of self-attention layer
    Fig. 2. Internal structure of self-attention layer
    Learnable PatchmatchNet structure
    Fig. 3. Learnable PatchmatchNet structure
    DTU dataset benchmark results. (a) Depth map; (b) confidence map; (c) point cloud result
    Fig. 4. DTU dataset benchmark results. (a) Depth map; (b) confidence map; (c) point cloud result
    Relationship between the overall error and GPU memory and running time, the image size is 1152×864. (a) Relationship between overall error and GPU memory consumption; (b) relationship between overall error and running time
    Fig. 5. Relationship between the overall error and GPU memory and running time, the image size is 1152×864. (a) Relationship between overall error and GPU memory consumption; (b) relationship between overall error and running time
    Depth map comparison of ablation experiment. (a) Depth map without improvement; (b) improved depth map; (c) GT
    Fig. 6. Depth map comparison of ablation experiment. (a) Depth map without improvement; (b) improved depth map; (c) GT
    Point cloud local magnification comparison map in the ablation experiment
    Fig. 7. Point cloud local magnification comparison map in the ablation experiment
    MethodAcc /mmComp /mmOverall /mm
    Camp0.8350.5540.695
    Furu0.6130.9410.777
    Tola0.3421.1900.766
    Gipuma0.2830.8730.578
    MVSNet0.3960.5270.462
    R-MVSNet0.3830.4520.417
    P-MVSNet0.4060.4340.420
    Point-MVSNet0.3420.4110.376
    Fast-MVSNet0.3360.4030.370
    CasMVSNet0.3250.3850.355
    CVP-MVSNet0.2960.4060.351
    M3VSNet0.6360.5310.583
    PatchmatchNet0.4270.2770.352
    Proposed method0.4270.2610.344
    Table 1. Test results of different methods on the DTU dataset
    MethodF-score
    COLMAP27.24
    R-MVSNet24.91
    CasMVSNet31.12
    PatchmatchNet32.31
    Proposed method32.72
    Table 2. Results of different methods on Tanks and Temples dataset
    ModuleAcc /mmComp /mmOverall /mmGPU /GBRunning time /sVertices
    MsFe0.4270.2770.35210.890.21051527707
    SA-MsFe0.4270.2610.3447.80.56252289896
    Table 3. Comparison of quantitative results of ablation experiment