אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

Deep Point Correspondence presents a new method for real-time non-rigid dense correspondence between point clouds based on structured shape construction. The method requires a fraction of the training data compared to previous techniques and presents better generalization capabilities. Until now, two main approaches have been suggested for the dense correspondence problem. The first is a spectral-based approach that obtains great results on synthetic datasets but requires mesh connectivity of the shapes and long inference processing time while being unstable in real-world scenarios. The second is a spatial approach that uses an encoder-decoder framework to regress an ordered point cloud for the matching alignment from an irregular input. Unfortunately, the decoder brings considerable disadvantages, as it requires a large amount of training data and struggles to generalize well in cross-dataset evaluations. DPC's novelty lies in its lack of a decoder component. Instead, we use latent similarity and the input coordinates themselves to construct the point cloud and determine correspondence, replacing the coordinate regression done by the decoder. Short bio: Dvir Ginzburg is a PhD candidate at Tel-Aviv University focusing on geometric deep learning.