Carolina Rovegno
3D Gaussian Splatting for UAV-Based Reconstruction of Urban Environments.
Rel. Andrea Bottino, Francesco Carlo Nex. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2025
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Abstract
UAV-based 3D reconstruction of large-scale environments has been largely adopted in different domains, starting with virtual reality and 3D documentation and moving on to more practical applications such as land analysis, urban area mapping and disaster management. The reconstruction techniques used are mainly based on two different approaches: with passive sensors, relying on image-only methodologies and with active sensors, which consist of laser-based methods (such as LiDAR). Image-based reconstruction was traditionally based on photogrammetric computer vision, but deep learning has recently innovated these techniques. Deep learning-based methods can learn how to represent three-dimensional scenes to generate realistic renderings from a sequence of images.
Among these approaches, Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have recently shown promising results in the generation of accurate rendering, but the quality of the generated 3D point cloud is often neglected
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