Learning Spectral Transform Network on 3D Surface for Non-rigid Shape Analysis

10/21/2018
by   Ruixuan Yu, et al.
0

Designing a network on 3D surface for non-rigid shape analysis is a challenging task. In this work, we propose a novel spectral transform network on 3D surface to learn shape descriptors. The proposed network architecture consists of four stages: raw descriptor extraction, surface second-order pooling, mixture of power function-based spectral transform, and metric learning. The proposed network is simple and shallow. Quantitative experiments on challenging benchmarks show its effectiveness for non-rigid shape retrieval and classification, e.g., it achieved the highest accuracies on SHREC14, 15 datasets as well as the Range subset of SHREC17 dataset.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset