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Using Neural Radiance Fields and Gaussian Splatting for 3D reconstruction of aircraft inspections

Bottema, Roos Eline LU (2024) In Master’s Theses in Mathematical Sciences FMAM05 20232
Mathematics (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:
author
Bottema, Roos Eline LU
supervisor
organization
course
FMAM05 20232
year
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}},
}