Link arXiv fix

README.md

@@ -4,7 +4,7 @@
 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. We will release the code upon paper acceptance.
 - [Webpage](https://iit-pavis.github.io/DiffAssemble/)
 - [Code](https://github.com/IIT-PAVIS/DiffAssemble)
-- [Arxiv](https://arxiv.org/abs/2402.19302")
+- [Arxiv](https://arxiv.org/abs/2402.19302)
 
 # Method
 ![Architecture](https://raw.githubusercontent.com/IIT-PAVIS/DiffAssemble/release/page/architecture.jpg)