While the area of structured indoor reconstruction has witnessed substantial progress in the past decade, current solutions are mostly limited to relatively simple environments with constrained shapes and provide very limited opportunities for seamlessly going from captured data to a dynamically modifiable representation. In AIN2, we aim to substantially advance this research field by proposing specialized solutions for rapidly capturing, exploring, and visually editing indoor environment starting from panoramic images. The project will tackle fundamental problems in the field of reality-based indoor reconstruction, modeling, exploration, leading to new methods, algorithms, and reference implementations. CRS4, in particular, will focus on advanced reconstruction and visualization methods starting from sparse panoramic images, focusing on data-driven solutions that derive hidden relations from large amounts of examples.
