Timezone: »

Poster
Exact Shape Correspondence via 2D graph convolution
Barakeel Fanseu Kamhoua · Lin Zhang · Yongqiang Chen · Han Yang · MA Kaili · Bo Han · Bo Li · James Cheng

Wed Nov 30 02:00 PM -- 04:00 PM (PST) @ Hall J #619
For exact 3D shape correspondence (matching or alignment), i.e., the task of matching each point on a shape to its exact corresponding point on the other shape (or to be more specific, matching at geodesic error 0), most existing methods do not perform well due to two main problems. First, on nearly-isometric shapes (i.e., low noise levels), most existing methods use the eigen-vectors (eigen-functions) of the Laplace Beltrami Operator (LBO) or other shape descriptors to update an initialized correspondence which is not exact, leading to an accumulation of update errors. Thus, though the final correspondence may generally be smooth, it is generally inexact. Second, on non-isometric shapes (noisy shapes), existing methods are generally not robust to noise as they usually assume near-isometry. In addition, existing methods that attempt to address the non-isometric shape problem (e.g., GRAMPA) are generally computationally expensive and do not generalise to nearly-isometric shapes. To address these two problems, we propose a 2D graph convolution-based framework called 2D-GEM. 2D-GEM is robust to noise on non-isometric shapes and with a few additional constraints, it also addresses the errors in the update on nearly-isometric shapes. We demonstrate the effectiveness of 2D-GEM by achieving a high accuracy of 90.5$\%$ at geodesic error 0 on the non-isometric benchmark SHREC16, i.e., TOPKIDS (while being much faster than GRAMPA), and on nearly-isometric benchmarks by achieving a high accuracy of 92.5$\%$ on TOSCA and 84.9$\%$ on SCAPE at geodesic error 0.

#### Author Information

##### Lin Zhang (HKUST)

I am a PhD student in the Computer Science & Engineering Department at Hong Kong University of Science and Technology. I am broadly interested in machine learning systems and applications, with a special focus on distributed deep learning, second-order optimization, and unsupervised learning on graphs (e.g., clustering and matching).