DOI QR코드

DOI QR Code

Assessment of Parallel Computing Performance of Agisoft Metashape for Orthomosaic Generation

정사모자이크 제작을 위한 Agisoft Metashape의 병렬처리 성능 평가

  • Han, Soohee (Dept. of Geoinformatics Engineering, Kyungil University) ;
  • Hong, Chang-Ki (Dept. of Geoinformatics Engineering, Kyungil University)
  • Received : 2019.10.29
  • Accepted : 2019.11.22
  • Published : 2019.12.31

Abstract

In the present study, we assessed the parallel computing performance of Agisoft Metashape for orthomosaic generation, which can implement aerial triangulation, generate a three-dimensional point cloud, and make an orthomosaic based on SfM (Structure from Motion) technology. Due to the nature of SfM, most of the time is spent on Align photos, which runs as a relative orientation, and Build dense cloud, which generates a three-dimensional point cloud. Metashape can parallelize the two processes by using multi-cores of CPU (Central Processing Unit) and GPU (Graphics Processing Unit). An orthomosaic was created from large UAV (Unmanned Aerial Vehicle) images by six conditions combined by three parallel methods (CPU only, GPU only, and CPU + GPU) and two operating systems (Windows and Linux). To assess the consistency of the results of the conditions, RMSE (Root Mean Square Error) of aerial triangulation was measured using ground control points which were automatically detected on the images without human intervention. The results of orthomosaic generation from 521 UAV images of 42.2 million pixels showed that the combination of CPU and GPU showed the best performance using the present system, and Linux showed better performance than Windows in all conditions. However, the RMSE values of aerial triangulation revealed a slight difference within an error range among the combinations. Therefore, Metashape seems to leave things to be desired so that the consistency is obtained regardless of parallel methods and operating systems.

본 연구에서는 SfM (Structure from Motion) 기술을 기반으로 항공삼각측량을 수행하고 3차원 포인트 클라우드를 생성하며 정사모자이크를 제작할 수 있는 Agisoft Metashape의 병렬처리 성능을 평가하였다. SfM의 속성상 상호표정에 해당하는 Align photos와 3차원 포인트 클라우드를 생성하는 Build dense cloud가 대부분의 시간을 차지하는데, Metashape에서는 이러한 과정에서 CPU (Central Processing Unit)의 다중코어와 함께 GPU (Graphics Processing Unit)를 이용하여 병렬처리를 수행할 수 있다. 세 가지 병렬처리 방법(CPU only, GPU only, CPU + GPU)과 두 가지 운영체제(Windows, Linux)를 조합하여 총 여섯 가지 조건으로 대용량 무인기 영상으로부터 정사모자이크를 제작하였다. 아울러 사용자의 개입 없이 자동화된 방법으로 영상에서 지상기준점을 인식하여 항공삼각측량의 RMSE (Root Mean Square Error)를 측정함으로써 각 조건에 따른 결과의 일관성을 평가하였다. 4220만 화소의 무인기 영상 521장으로부터 정사모자이크를 제작한 결과, 본 연구에서 사용한 시스템에서는 CPU와 GPU의 조합이 가장 나은 성능을 나타내었고 모든 조건에서 Linux가 Windows보다 나은 성능을 나타내었다. 그러나 항공삼각측량의 RMSE를 측정한 결과, 각 설정에 따른 RMSE 값에서 오차 범위 안에서 미세한 차이가 나타났다. 따라서 Metashape는 운영체제 및 병렬처리 여부에 관계없이 동일한 결과가 도출되도록 개선할 여지가 있는 것으로 판단된다.

Keywords

References

  1. Arya, S., Mount, D.M., Netanyahu, N.S., Silverman, R., and Wu, A.Y. (2007), An optimal algorithm for approximate nearest neighbor searching fixed dimensions, Journal of the ACM, Vol. 45, No. 6, pp. 891-923. https://doi.org/10.1145/293347.293348
  2. Bianco, S., Ciocca, G., and Marelli, D. (2018), Evaluating the performance of structure from motion pipelines, Journal of Imaging, Vol. 4, No. 8:98. https://doi.org/10.3390/jimaging4080098
  3. Fischler, M.A. and Bolles, R.C. (1981), Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography, Communications of the ACM, Vol. 24, No. 6, pp. 381-395. https://doi.org/10.1145/358669.358692
  4. Gao, M., Xu, X., Klinger, Y., Woerd, J. van der, and Tapponnier, P. (2017), High-resolution mapping based on an unmanned aerial vehicle (UAV) to capture paleoseismic offsets along the Altyn-Tagh fault, China, Scientific Reports, Vol. 7:8281. https://doi.org/10.1038/s41598-017-08119-2
  5. Han, S. (2017), High-resolution and high-definition image acquisition using UAV and high-precision aerial triangulation, Journal of Korean Society for Geospatial Information Science, Vol. 25, No. 3, pp. 101-109. (in Korean with English abstract) https://doi.org/10.7319/kogsis.2017.25.3.101
  6. Han, S. and Hong, C.-K. (2017), Acquisition of subcentimeter GSD images using UAV and analysis of visual resolution, Journal of the Korean Society of Survey, Geodesy, Photogrammetry, and Cartography, Vol. 35, No. 6, pp. 563-572. (in Korean with English abstract) https://doi.org/10.7848/ksgpc.2017.35.6.563
  7. Kim, H., Lee, J., Ahn, E., Cho, S., Shin, M., and Sim, S.-H. (2017), Concrete crack identification using a UAV incorporating hybrid image processing, Sensors, Vol. 17, No. 9:2052. https://doi.org/10.3390/s17092052
  8. Laporte-Fauret, Q., Marieu, V., Castelle, B., Michalet, R., Bujan, S., and Rosebery, D. (2019), Low-cost UAV for high-resolution and large-scale coastal dune change monitoring using photogrammetry, Journal of Marine Science and Engineering, Vol. 7, No. 3:63. https://doi.org/10.3390/jmse7030063
  9. Lee, J., Choi, H., and Kim, D. (2019), Accuracy evaluation of stereo plotting with medium format camera image acquired by a drone, Fall Conference of Korean Society for Geospatial Information Science, 31 May-1 June, Busan, South Korea. (in Korean)
  10. Lowe, D.G. (2004), Distinctive image features from scaleinvariant keypoints, International Journal of Computer Vision, Vol. 60, No. 2, pp. 91-110. https://doi.org/10.1023/B:VISI.0000029664.99615.94
  11. Luhmann, T., Chizhova, M., Gorkovchuk, D., Hastedt, H., Chachava, N., and Lekveishvili, N. (2019), Combination of terrestrial laserscanning, UAV and close-range photogrammetry for 3D reconstruction of complex churches in Georgia, 2nd International Conference of Geomatics and Restoration, 8-10 May, Milan, Italy, pp. 753-761.
  12. Micheletti, N., Chandler, J., and Lane, S.N. (2015), Structure from motion (SFM) photogrammetry, In: Clarke, L.E. and Nield, J.M. (Eds.), Geomorphological Techniques (Online Edition), British Society for Geomorphology, London, Chap. 2, Sec. 2.2.
  13. Singh, A.K., Swarup, A., Agarwal, A., and Singh, D. (2017), Vision based rail track extraction and monitoring through drone imagery, ICT Express, In press.
  14. Westoby, M.J., Brasington, J., Glasser, N.F., Hambrey, M.J., and Reynolds, J.M. (2012), 'Structure-from-Motion' photogrammetry: A low-cost, effective tool for geoscience applications, Geomorphology, Vol. 179, pp. 300-314. https://doi.org/10.1016/j.geomorph.2012.08.021
  15. Zhang, Y., Yuan, X., Li, W., and Chen, S. (2019), Automatic power line inspection using UAV images, Remote Sensing, Vol. 9, No. 8:824. https://doi.org/10.3390/rs9080824

Cited by

  1. 오픈소스 기반 UAS 영상 재현 알고리즘 및 필터링 기법 비교 vol.50, pp.2, 2019, https://doi.org/10.22640/lxsiri.2020.50.2.155
  2. Seven Different Lighting Conditions in Photogrammetric Studies of a 3D Urban Mock-Up vol.14, pp.23, 2019, https://doi.org/10.3390/en14238002