DiffAssemble: A Unified Graph-Diffusion Model for 2D and 3D Reassembly
<|reference_start|>DiffAssemble: A Unified Graph-Diffusion Model for 2D and 3D Reassembly: Reassembly tasks play a fundamental role in many fields and multiple approaches exist to solve specific reassembly problems. In this context, we posit that a general unified model can effectively address them all, irrespective of the input data type (images, 3D, etc.). We introduce DiffAssemble, a Graph Neural Network (GNN)-based architecture that learns to solve reassembly tasks using a diffusion model formulation. Our method treats the elements of a set, whether pieces of 2D patch or 3D object fragments, as nodes of a spatial graph. Training is performed by introducing noise into the position and rotation of the elements and iteratively denoising them to reconstruct the coherent initial pose. DiffAssemble achieves state-of-the-art (SOTA) results in most 2D and 3D reassembly tasks and is the first learning-based approach that solves 2D puzzles for both rotation and translation. Furthermore, we highlight its remarkable reduction in run-time, performing 11 times faster than the quickest optimization-based method for puzzle solving. Code available at https://github.com/IIT-PAVIS/DiffAssemble<|reference_end|>
@article{scarpellini2024diffassemble:,
    title={DiffAssemble: A Unified Graph-Diffusion Model for 2D and 3D Reassembly},
    author={Gianluca Scarpellini, Stefano Fiorini, Francesco Giuliari, Pietro
  Morerio, Alessio Del Bue},
    journal={arXiv preprint arXiv:2402.19302},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.19302},
    primaryClass={cs.CV}
}