AUTHORS: Giulio FEDERICO, Fabio CARRARA, Giuseppe AMATO, and Marco DI BENEDETTO

Work Package: WP10 – ReTINA

URL: Pageflex Server [document: D-VTT-2555FF3A_00001]

Keywords: Generative AI, Computer Graphics, Denoising Diffusion Probabilistic Model, Gaussian Splatting, NeRF, Signed Distance Field, Video Reconstruction, Deep Learning, Machine Learning, Artificial Intelligence, Text-to-3D, Image-to-3D, Urban Environment, Score Distillation Sampling

Abstract
The reconstruction of large-scale real outdoor environments is crucial for promoting the adoption of extended Reality (XR) in industrial and entertainment sectors. This task often requires significant resources such as depth cameras, LiDAR sensors, drones, and others, alongside traditional data processing pipelines like Structure-from-Motion (SfM), which demand extensive computational resources, thus preventing real-time processing. Additional constraints arise from the limited accessibility to the aforementioned resources. While 3D laser scanners (e.g., LiDAR) are precise and fast, they are expensive, often bulky especially the high-quality models and their effectiveness is contingent on the type of environment being scanned. Depth sensors offer a more affordable and compact alternative; however, due to their limited range, they are ideal only for indoor settings.
Photogrammetry, while capable of producing high-quality results at a lower cost, can be time consuming and computationally intensive. It also suffers from limited accuracy, strong dependence on lighting conditions, and the need for numerous photos from various angles that can be not always easily accessible. (…)