Using Neural Radiance Fields and Gaussian Splatting for 3D reconstruction of aircraft inspections
(2024) In Master’s Theses in Mathematical Sciences FMAM05 20232Mathematics (Faculty of Engineering)
- Abstract
- The rapid evolution of machine learning techniques has revolutionized computer vision, particularly with the introduction of Neural Radiance Fields (NeRF) and the optimization of 3D Gaussians for rendering novel scene views. These methods, such as NeRF and Gaussian Splatting,
have demonstrated success in synthetic data scenarios with consistent lighting and well-captured
scenes. This research explores the feasibility of applying these techniques to images captured by
drones conducting aircraft inspections, aiming to automate and optimize the aviation industry.
Motivated by the need for more efficient inspection processes, various models were examined and
parameters adjusted to assess the performance of NeRFs and Gaussian Splatting in... (More) - The rapid evolution of machine learning techniques has revolutionized computer vision, particularly with the introduction of Neural Radiance Fields (NeRF) and the optimization of 3D Gaussians for rendering novel scene views. These methods, such as NeRF and Gaussian Splatting,
have demonstrated success in synthetic data scenarios with consistent lighting and well-captured
scenes. This research explores the feasibility of applying these techniques to images captured by
drones conducting aircraft inspections, aiming to automate and optimize the aviation industry.
Motivated by the need for more efficient inspection processes, various models were examined and
parameters adjusted to assess the performance of NeRFs and Gaussian Splatting in real-world
scenarios. Despite the visual shortcomings observed in both NeRF and Gaussian Splatting,
Gaussian Splatting came out as more promising for inspection images, outperforming NeRF
visually. Quantitative results are presented, using metrics such as SSIM, PSNR and LPIPS to
provide a more concrete understanding of the visual performance of the methods. While Gaussian Splatting exhibits promise, it is essential to acknowledge that the current quality of the
output falls short of production standards. Looking ahead, with both methods being relatively
new, substantial improvements in performance and broader applications are anticipated in the
near future. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9149081
- author
- Bottema, Roos Eline LU
- supervisor
-
- Viktor Larsson LU
- Erik Tegler LU
- organization
- course
- FMAM05 20232
- year
- 2024
- type
- H2 - Master's Degree (Two Years)
- subject
- publication/series
- Master’s Theses in Mathematical Sciences
- report number
- LUTFMA-3523-2024
- ISSN
- 1404-6342
- other publication id
- 2024:E4
- language
- English
- id
- 9149081
- date added to LUP
- 2024-03-12 11:41:17
- date last changed
- 2024-03-12 11:41:17
@misc{9149081, abstract = {{The rapid evolution of machine learning techniques has revolutionized computer vision, particularly with the introduction of Neural Radiance Fields (NeRF) and the optimization of 3D Gaussians for rendering novel scene views. These methods, such as NeRF and Gaussian Splatting, have demonstrated success in synthetic data scenarios with consistent lighting and well-captured scenes. This research explores the feasibility of applying these techniques to images captured by drones conducting aircraft inspections, aiming to automate and optimize the aviation industry. Motivated by the need for more efficient inspection processes, various models were examined and parameters adjusted to assess the performance of NeRFs and Gaussian Splatting in real-world scenarios. Despite the visual shortcomings observed in both NeRF and Gaussian Splatting, Gaussian Splatting came out as more promising for inspection images, outperforming NeRF visually. Quantitative results are presented, using metrics such as SSIM, PSNR and LPIPS to provide a more concrete understanding of the visual performance of the methods. While Gaussian Splatting exhibits promise, it is essential to acknowledge that the current quality of the output falls short of production standards. Looking ahead, with both methods being relatively new, substantial improvements in performance and broader applications are anticipated in the near future.}}, author = {{Bottema, Roos Eline}}, issn = {{1404-6342}}, language = {{eng}}, note = {{Student Paper}}, series = {{Master’s Theses in Mathematical Sciences}}, title = {{Using Neural Radiance Fields and Gaussian Splatting for 3D reconstruction of aircraft inspections}}, year = {{2024}}, }