A Comparative Study of 3D Modeling Techniques using Aerial Photography: Performance Analysis for Autonomous Drone Flight Path Planning

Yoojeong Song; Jinpyo Jeon; Sang Ik Han

doi:10.22680/kasa2026.18.1.059

All Issue

2026 Vol.18, Issue 1 Preview Page Next Page

A Comparative Study of 3D Modeling Techniques using Aerial Photography: Performance Analysis for Autonomous Drone Flight Path Planning 항공사진 기반 3D 모델링 기법 비교 연구: 드론 자율비행 경로 설정을 위한 성능 분석

31 March 2026. pp. 59-67

PDF XML

Abstract

This study explores the need for accurate and reliable 3D environment models that support safe autonomous navigation of unmanned aerial vehicles (UAVs). To evaluate suitable modeling techniques, we compare two representative approaches: the traditional Structure-from-Motion (SfM) and Multi-View Stereo (MVS) pipeline, which produces a 3D mesh, and the recently developed 3D Gaussian Splatting (3D GS) method, which expresses a scene using a large set of 3D Gaussians and renders it through differentiable splatting. Using a dataset of about 500 aerial images captured over the Semyung University campus, we construct both 3D models and evaluate their effectiveness by integrating them into an RRT*-based global path planning framework. Quantitative analysis reveals distinct performance differences between the two models: the 3D Mesh model generates shorter paths, fewer waypoints, and much faster planning time, while the 3D GS model produces longer paths, more waypoints, and slower planning time. These results suggest that the 3D Mesh model is more efficient and suitable for pre-flight planning, whereas the 3D GS model—despite higher computational cost—offers richer environmental detail that can support high-precision obstacle avoidance. Overall, this study highlights how the choice of 3D modeling can critically influence UAV navigation performance, particularly in terms of environmental awareness, obstacle detection fidelity, and operational safety.

Keywords

3D Mesh(3차원 메쉬)

3D Gaussian splatting(3차원 가우시안 스플래팅)

Autonomous navigation(자율 항법)

Path planning(경로 설정)

Photogrammetry(사진측량)

References

Nex, F. and Remondino, F., 2014, “UAV for 3D mapping applications: A review,” Applied Geomatics. vol. 6, no. 1, pp. 1~15.

10.1007/s12518-013-0120-x

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.

10.1016/j.geomorph.2012.08.021

Berra, E. F. and Peppa, M. V., 2020, “Advances and Challenges of UAV SFM MVS Photogrammetry and Remote Sensing: Short Review,” 2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS), Santiago, Chile, 2020, pp. 533~538.

10.1109/LAGIRS48042.2020.9285975

Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., and Ng, R., 2020, “NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis,” Proceedings of the European Conference on Computer Vision (ECCV) 2020, pp. 405~421.

10.1007/978-3-030-58452-8_24

Kerbl, B., Kopanas, G., Leimkühler, T., and Drettakis, G., 2023, “3D Gaussian Splatting for Real-Time Radiance Field Rendering,” ACM Transactions on Graphics, Vol. 42, No. 4, Article 94, 2023.

10.1145/3592433

Zhang, Z., Wang, Q., Sun, Y., and Liu, Y., 2024, “LP-3DGS: Learning to Prune 3D Gaussian Splatting,” Advances in Neural Information Processing Systems (NeurIPS), pp. 1~12.

Barron, J.T., Mildenhall, B., Tancik, M., Hedman, P., Martin-Brualla, R., and Srinivasan, P. P., 2022, “Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1435~1445.

10.1109/ICCV48922.2021.00580

이재원, 김덕인, 한영준, 박준형, 2015, “UAV 기반 영상 취득 및 SfM/MVS 기법을 이용한 3차원 모델링 정확도 분석,” 한국측량학회지, Vol. 33, No. 3, pp. 241~250.

J.-M. Frahm, B. Gherardi, and G. D. Vogiatzis, 2013, “VisualSfM: A Visual Structure from Motion System,” arXiv preprint arXiv:1310.2762.

F. Remondino, F. Menna, and F. Nocerino, 2012, “A review of photogrammetry and remote sensing applications for surveying and mapping of cultural heritage and built environments,” Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., Vol. 39, pp. 267~273.

A. Hornung, K. M. Wurm, M. Bennewitz, C. Stachniss, and W. Burgard, 2013, “OctoMap: An Efficient Probabilistic 3D Mapping Framework Based on Octrees,” Autonomous Robots, Vol. 34, No. 3, pp. 189~206.

10.1007/s10514-012-9321-0

S. Karaman and E. Frazzoli, 2011, “Sampling-based algorithms for optimal motion planning,” The International Journal of Robotics Research, Vol. 30, No. 7, pp. 846-894.

10.1177/0278364911406761

J. Borenstein and Y. Koren, 1991, “The vector field histogram-fast obstacle avoidance for mobile robots,” IEEE Transactions on Robotics and Automation, Vol. 7, No. 3, pp. 278~288.

10.1109/70.88137

L. Lu, C. Liu, Y. Wang, P. Oh, and Z. Chen, “UAV Path Planning and Collision Avoidance: A Review,” in Proc. Int. Conf. Unmanned Aircr. Syst. (ICUAS), 2018, pp. 1002~1011.

J. Chen, M. Li, Z. Yuan, and Q. Gu, Jun. 2020, “An improved A∗ algorithm for UAV path planning problems,” in Proc. IEEE 4th Inf. Technol., Netw., Electron. Autom. Control Conf. (ITNEC), Vol. 1, pp. 958~962.

10.1109/ITNEC48623.2020.9084806

F. Kong, Q. Wang, S. Gao, and H. Yu, 2023, “B-APFDQN: A UAV path planning algorithm based on deep Q-network and artificial potential field,” IEEE Access, Vol. 11, pp. 44051~44064.

10.1109/ACCESS.2023.3273164

J. Chen, F. Ling, Y. Zhang, T. You, Y. Liu, and X. Du, “Coverage path planning of heterogeneous unmanned aerial vehicles based on ant colony system,” Swarm Evol. Comput., Vol. 69, Mar. 2022, Art. No. 101005.

10.1016/j.swevo.2021.101005

Information

Publisher :Korean Auto-vehicle Safety Association
Publisher(Ko) :한국자동차모빌리티안전학회
Journal Title :Journal of Auto-vehicle Safety Association
Journal Title(Ko) :자동차안전학회지
Volume : 18
No :1
Pages :59-67
Received Date : 2025-11-24
Revised Date : 2025-12-10
Accepted Date : 2025-12-11
DOI :https://doi.org/10.22680/kasa2026.18.1.059

Journal of Auto-vehicle Safety Association ISSN:2005-9396(Print) 자동차안전학회지

All Issue