DiffAssemble: A Unified Graph-Diffusion Model for 2D and 3D Reassembly

• URL: http://arxiv.org/abs/2402.19302v1
• Date: Thu, 29 Feb 2024 16:09:12 GMT
• Title: DiffAssemble: A Unified Graph-Diffusion Model for 2D and 3D Reassembly
• Authors: Gianluca Scarpellini, Stefano Fiorini, Francesco Giuliari, Pietro Morerio, Alessio Del Bue
• Abstract summary: We introduce DiffAssemble, a Graph Neural Network (GNN)-based architecture that learns to solve reassembly tasks. Our method treats the elements of a set, whether pieces of 2D patch or 3D object fragments, as nodes of a spatial graph. We highlight its remarkable reduction in run-time, performing 11 times faster than the quickest optimization-based method for puzzle solving.
• Score: 21.497180110855975
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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

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