Pixel Club: Statistical Non-Rigid Shape Correspondence ​

דובר:
עמנואל רודולה (USI, לוגנו)
תאריך:
יום ראשון, 27.11.2016, 11:00
מקום:
חדר 337, בניין טאוב למדעי המחשב

Many algorithms for the computation of correspondences​ ​between deformable shapes rely on some variant of​ ​nearest neighbor matching in a descriptor space. Such are,​ ​for example, various point-wise correspondence recovery​​ algorithms used as a post-processing stage in the functional​ ​correspondence framework. Such frequently used techniques​ ​implicitly make restrictive assumptions (e.g., nearisometry)​ ​on the considered shapes and in practice suffer from lack of accuracy and result in poor surjectivity. We​ ​propose an alternative recovery technique capable of guaranteeing​ ​a bijective correspondence and producing significantly​ ​higher accuracy and smoothness. Unlike other methods our approach does not depend on the assumption that​ ​the analyzed shapes are isometric. We derive the proposed​ ​method from the statistical framework of kernel density estimation​ ​and demonstrate its performance on several challenging​ ​deformable 3D shape matching datasets. ​​

Bio:
Emanuele is a post-doctoral researcher at Università della Svizzera Italiana (USI Lugano) since February 2016, where he works in the group led by Prof. Michael Bronstein. Before that, he was an Alexander von Humboldt Fellow in Prof. Daniel Cremers' Computer Vision lab at TU Munich (2013-2016) and a JSPS Research Fellow at The University of Tokyo (Intelligent Systems and Informatics Lab, 2013). His research interests focus on Shape Analysis, matching, and reconstruction, and co-authored around 40 papers on these topics. He received a number of awards, including the Best Student Paper Award at 3DPVT 2010, the Best Paper Award at VMV 2015, and the Best Paper Award at SGP 2016. He has been serving in the program committees of the top rated conferences in computer vision (CVPR, ICCV, ECCV, ACCV, etc.), served as Area Chair at 3DV 2016, founded and chaired the first ECCV workshop on Geometry Meets Deep Learning (GMDL 2016), organized two SHREC 2016 contests, and was recognized as Outstanding Reviewer at CVPR (2013, 2015, 2016), ICCV (2015), and ECCV (2014, 2016). He gave tutorials and short courses in multiple occasions at EUROGRAPHICS, ECCV, and SIGGRAPH Asia. Emanuele's work on 3D reconstruction was featured by the national Italian television (RAI - Cose dell'altro Geo) in 2012.

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