Technical Papers

Date/Time: 04 – 13 December 2020
All presentations are available in the virtual platform on-demand.

Lecturer(s):
Jing Ren, KAUST, Saudi Arabia
Simone Melzi, Ecole Polytechnique, France
Maks Ovsjanikov, Ecole Polytechnique, France
Peter Wonka, KAUST, Saudi Arabia

Bio:

Description: In this paper we propose an approach for automatically computing multiple high-quality near-isometric maps between a pair of 3D shapes. Our method is fully automatic and does not rely on user-provided landmarks or descriptors. This allows us to analyze the full space of maps and extract multiple diverse and accurate solutions, rather than optimizing for a single optimal correspondence as done in previous approaches. To achieve this, we first propose a compact tree structure based on the spectral map representation for encoding and enumerate possible rough initializations, and a novel efficient approach for refining them to dense pointwise maps. This leads to the first method capable of both producing multiple high-quality correspondences across shapes and revealing the symmetry structure of a shape without a priori information. In addition, we demonstrate through extensive experiments that our method is robust and results in more accurate correspondences than state-of-the-art for shape matching and symmetry detection.