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Advancing 3D Scene Reconstruction: Techniques, Pipelines, and Applications

Tian, Xuening LU (2024) MAMM15 20241
Department of Design Sciences
Abstract
This master thesis is carried out at Sony Nordic which aims to investigate state-of-the-art methods on 3D Scene reconstruction and explores the potential utilization in the industry. The project addresses challenges in reconstructing complex scenes using both static camera setups which consider scenarios with freely moving rigid objects and freely moving cameras. Throughout the research, several key questions were answered, resulting in a robust pipeline including image capture, camera calibration, foreground segmentation, camera estimation, and model training. The reconstruction utilizes technologies such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting. The result of this work demonstrates the feasibility and highlights the... (More)
This master thesis is carried out at Sony Nordic which aims to investigate state-of-the-art methods on 3D Scene reconstruction and explores the potential utilization in the industry. The project addresses challenges in reconstructing complex scenes using both static camera setups which consider scenarios with freely moving rigid objects and freely moving cameras. Throughout the research, several key questions were answered, resulting in a robust pipeline including image capture, camera calibration, foreground segmentation, camera estimation, and model training. The reconstruction utilizes technologies such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting. The result of this work demonstrates the feasibility and highlights the potential challenges of 3D reconstruction under various camera settings. Additionally, we propose several applications that could benefit from these advancements, depending on the scenarios. (Less)
Please use this url to cite or link to this publication:
author
Tian, Xuening LU
supervisor
organization
course
MAMM15 20241
year
type
H2 - Master's Degree (Two Years)
subject
keywords
3D reconstruction, Computer Vision, Neural Radiance Fields, Gaussian Splatting, Rendering, Deep Learning
language
English
id
9162342
date added to LUP
2024-06-13 08:11:25
date last changed
2024-06-13 08:11:25
@misc{9162342,
  abstract     = {{This master thesis is carried out at Sony Nordic which aims to investigate state-of-the-art methods on 3D Scene reconstruction and explores the potential utilization in the industry. The project addresses challenges in reconstructing complex scenes using both static camera setups which consider scenarios with freely moving rigid objects and freely moving cameras. Throughout the research, several key questions were answered, resulting in a robust pipeline including image capture, camera calibration, foreground segmentation, camera estimation, and model training. The reconstruction utilizes technologies such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting. The result of this work demonstrates the feasibility and highlights the potential challenges of 3D reconstruction under various camera settings. Additionally, we propose several applications that could benefit from these advancements, depending on the scenarios.}},
  author       = {{Tian, Xuening}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Advancing 3D Scene Reconstruction: Techniques, Pipelines, and Applications}},
  year         = {{2024}},
}