5 papers
E-M3RF: An Equivariant Multimodal 3D Re-assembly Framework
Adeela Islam, Stefano Fiorini, Manuel Lecha +4
3D reassembly is a fundamental geometric problem, and in recent years it has increasingly been challenged by deep learning methods rather than classical optimization. While learnin…
ReassembleNet: Learnable Keypoints and Diffusion for 2D Fresco Reconstruction
Adeela Islam, Stefano Fiorini, Stuart James +2
The task of reassembly is a significant challenge across multiple domains, including archaeology, genomics, and molecular docking, requiring the precise placement and orientation o…
Sheaves Reloaded: A Directional Awakening
Stefano Fiorini, Hakan Aktas, Iulia Duta +4
Sheaf Neural Networks (SNNs) represent a powerful generalization of Graph Neural Networks (GNNs) that significantly improve our ability to model complex relational data. While dire…
Maps from Motion (MfM): Generating 2D Semantic Maps from Sparse Multi-view Images
Matteo Toso, Stefano Fiorini, Stuart James +1
World-wide detailed 2D maps require enormous collective efforts. OpenStreetMap is the result of 11 million registered users manually annotating the GPS location of over 1.75 billio…
Re-assembling the past: The RePAIR dataset and benchmark for real world 2D and 3D puzzle solving
Theodore Tsesmelis, Luca Palmieri, Marina Khoroshiltseva +20
This paper proposes the RePAIR dataset that represents a challenging benchmark to test modern computational and data driven methods for puzzle-solving and reassembly tasks. Our dat…