Journals Articles Conferences News About CLP
Submit a paper Email Alert
Search
Search
Articles
Journals
News
Advanced Search
Top Searches
2D MaterialsTransformation opticsQuantum PhotonicsSmall lasersSemiconductor UV PhotonicsSupersymmetric microring laser arrays

Laser & Optoelectronics Progress, Volume. 62, Issue 6, 0615014(2025)

Three-Dimensional Reconstruction Model for Unmanned Aerial Vehicle Images Combining Spatial Geometric Information and Global Features

Qiming Jin1,2、*, Feng Wang3,4, Juanjuan Yang3,5, Yang Pang3,4, and Jianwu Dang1,2
Author Affiliations
  • 1School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, Gansu , China
  • 2National Virtual Simulation Experiment Teaching Center for Rail Transit Information and Control, Lanzhou 730070, Gansu , China
  • 3Gansu Luqiao Feiyu Transportation Facilities Co. Ltd., Lanzhou 730070, Gansu , China
  • 4Gansu Xinnetcom Technology Information Co., Ltd., Lanzhou 730070, Gansu , China
  • 5Gansu Provincial Highway Transportation Construction Group Co., Ltd., Lanzhou 730070, Gansu , China
    • show less
    AbstractGet PDF(in Chinese)
    Figures&Tables (14)
    Equations (15)
    References (27)
    Tools
    Paper Information
    Topics
    Figures & Tables(14)
    Main framework

    Fig. 1. Main framework

    Download full size
    Incremental SFM sparse reconstruction model framework

    Fig. 2. Incremental SFM sparse reconstruction model framework

    Download full size
    Descriptor enhancement mode

    Fig. 3. Descriptor enhancement mode

    Download full size
    AFT model

    Fig. 4. AFT model

    Download full size
    The SwiGLU model

    Fig. 5. The SwiGLU model

    Download full size
    Datesets. (a) dataset 1; (b) dataset 2; (c) NPU_Central

    Fig. 6. Datesets. (a) dataset 1; (b) dataset 2; (c) NPU_Central

    Download full size
    Point cloud reconstruction visualization results

    Fig. 7. Point cloud reconstruction visualization results

    Download full size
    Comparison of point cloud results from self-collected datasets

    Fig. 8. Comparison of point cloud results from self-collected datasets

    Download full size
    Comparison of public dataset point cloud results

    Fig. 9. Comparison of public dataset point cloud results

    Download full size

    • Table 1. Experimental environment configuration

      View table

      Table 1. Experimental environment configuration

      NameParameters and information
      GPUNvidia GeForce RTX4080
      CPUIntel Core i7-13700kf@5.2 GHz
      RAM32 GB
      Operating systemUbuntu 20.04 LTS； Windows 11

    • Table 2. Algorithm performance comparison

      View table

      Table 2. Algorithm performance comparison

      DatasetAlgorithmSparse point↑Observation point↑Track length↑Reproj error↓
      dataset 1SIFT1578805369883.401.39
      ORB34383803552.331.49
      SuperPoint1697225940483.501.32
      SOSNet1689385710103.381.30
      SIFT+Boost1701176108783.591.15
      ORB+Boost39519889902.251.40
      dataset 2SIFT1137764582704.021.52
      ORB470281183162.511.55
      SuperPoint1345125835774.341.39
      SOSNet1316525634704.281.38
      SIFT+Boost1398176023354.311.35
      ORB+Boost496171385182.791.38
      NPU_CentralSIFT676254825657.130.79
      ORB2037837006183.430.88
      SuperPoint395552636806.661.25
      SOSNet505163288596.510.98
      SIFT+Boost764395748387.520.68
      ORB+Boost2102427236773.840.69

    • Table 3. Comparison of ablation experimental data

      View table

      Table 3. Comparison of ablation experimental data

      MethodSelf-mapping layerCross-mapping layerSparse pointReproj error
      SIFT1578801.39
      √1623051.38
      √1675491.35
      √√1701171.35
      ORB343831.49
      √354921.49
      √387511.47
      √√395191.46

    • Table 4. Feature extraction running time

      View table

      Table 4. Feature extraction running time

      Methoddataset 1dataset 2NPU_Central
      SIFT25.72811.86610.351
      ORB26.33813.09615.791
      SuperPoint27.57819.78814.559
      SOSNet26.35418.35413.854
      SIFT+Boost27.39913.80912.345
      ORB+Boost28.58912.71720.957

    • Table 5. Sparse reconstruction running time

      View table

      Table 5. Sparse reconstruction running time

      Methoddataset 1dataset 2NPU_Central
      SIFT382.538839.2992372.982
      ORB292.046521.1943637.606
      SuperPoint539.1471047.8623885.129
      SOSNet482.858853.3213186.541
      SIFT+Boost401.469867.4802836.873
      ORB+Boost289.590414.1353563.741
    Tools

    Get Citation

    Copy Citation Text

    Qiming Jin, Feng Wang, Juanjuan Yang, Yang Pang, Jianwu Dang. Three-Dimensional Reconstruction Model for Unmanned Aerial Vehicle Images Combining Spatial Geometric Information and Global Features[J]. Laser & Optoelectronics Progress, 2025, 62(6): 0615014

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Set citation alerts for article
    Save article for my favorites
    Paper Information

    Category: Machine Vision

    Received: Jul. 25, 2024

    Accepted: Sep. 10, 2024

    Published Online: Mar. 13, 2025

    The Author Email:

    DOI:10.3788/LOP241743

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology